Cambridgeshire County Council (CCC) October 2009

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1 Cambridgeshire County Council (CCC) October 2009

2 QM Issue/revision Issue 1 Revision 1 Revision 2 Revision 3 Revision 4 Remarks Draft v1.0 for external review Partial draft v2.0: and chapter 7 to DfT V2.0 Chapters 1-7 plus appendices only V2.1 Completed Chapter 8 PT Validation, small revisions to Chapters 1-7 V2.2 Updates following CCC review. Date Prepared by Signature Checked by Signature Authorised by Signature Project number File reference MDVR1 MDVR2.0 MDVR2.0 MDVR2.1 \\ser02cam1uk.uk.w spgroup.com\pdisk\ P12\1237 CCC TIF\TEXT\REPORT S\Implementation\R elease 5 - Draft of MDVR 2.2\CSRM_TDM_M DVR2.2.doc WSP Development and Transportation Hills Road Cambridge CB2 1LA Tel: +44 (0) Fax: +44 (0) WSP UK Limited Registered Address Buchanan House, Holborn, London, EC1N 2HS, UK Reg No England WSP Group plc Offices worldwide

3 Contents 1.1 Background Cambridge Sub Regional Model (CSRM) Study area Overview of the modelling framework Structure of the report 6! 2.1 Overview of the TDM Zoning Dimensions and units Demand segmentation Mode definitions Choice model structure Time periods The Off-Peak period 28 " # $%& ' 3.1 Overview Link types and network modes / stages of travel Bus network and services Rail network and services Guided Bus network and services Park and Ride (bus and Guided Bus) network and services Walk and cycle Intrazonal distances and times External network 45 ( ) (! 4.1 Overview Generalised cost formulation Vehicle operating costs Vehicle occupancies Values of time Parking charges Public transport fares 54

4 4.8 Public transport access and wait times Weighting of time 58 * +, 5.1 Overview Population inputs for trip generation Planning data inputs for trip attractions Home-based trip generation Generation of non home-based trips Zone matching 67 + )!, 6.1 Overview Parameters for discrete choice models Walk and Cycle parameters Time of Day and Mode Choice parameters Sub-mode choice Cost Damping Calibration process Overview of Incremental modelling approach Data for incremental modelling Link between Transport demand model and local highway model Obtaining Distance, cost and time skims Converting 12-hour person trip matrices to peak hour highway matrices Pivoting the Highway Matrix Goods Vehicle demand Convergence 88! -' 7.1 Overview Data availability Mode and time of day split Trips across RSI cordons Car trip time of day split versus RSI Comparison of synthetic Light Vehicle Trips with Local Highway Model Realism Testing Methodology Realism Test Results 104 Cambridge Sub Regional Model (CSRM) Transport Demand and Public Transport Model Development and Validation Report 4

5 7.9 Conclusion Overview Bus survey DESIGN Back-casting and seasonal correction Model values for comparison Model validation against bus survey data Public transport network verification (Timetabled journey times) Cambridgeshire County Council bus monitoring Park and Ride LONDON AREA Travel Survey Rail demand at stations Cycle demand (cordons / screenlines) Summary of PT and cycle validation 135 ' "! & /& 0. & /1 2 & / 3%. & / 1 4 & /5 & & /6 7& 386&135 Table 2.1: Number of zones within the model by district 11 Table 2.2: Dimensions and units 12 Table 2.3: Trip purposes in TDM 13 Table 2.4: Purpose segmentation in each submodel 13 Table 2.5: Grouped Socio-economic Category (GSeC) defined from NS-SeC 15 Table 2.6: HBW trips segmentation by choice stage 16 Table 2.7: HBEd trips segmentation by choice stage 16 Table 2.8 HB and NHB EB trips segmentation by choice stage 17 Table 2.9: Discretionary (HBPS, HBRV and NHBO) trips segmentation by choice stage 18 Table 2.10: Transport modes in the CSRM model 20 Table 2.11: Access Modes available for Main Modes 21 Table 2.12: Summary of choices modelled within the CSRM 22 Table 2.13: TDM demand units 24 Table 2.14: Time periods represented by each model component 25 Table 2.15: Relationship between costs by time of travel and tour cost 26 Table 2.16: Return trips given time of outbound trip 27 Table 3.1: Stages of travel defined in CSRM 32 Table 3.2: Outline of link types within multi-modal networks 33 Table 4.1: TDM base year (2006) vehicle operating costs (VOC) 48 5 Cambridge Sub Regional Model (CSRM) Transport Demand and Public Transport Model Development and Validation Report

6 Table 4.2: Vehicle occupancies for the year Table 4.3: Values of time for CSRM in 2006 prices and values 50 Table 4.4 Car park charges in Cambridge (2007 pence) 51 Table 4.5: Assumed parking charges in urban areas outside Cambridge (2007 pence) 51 Table 4.6: Parking charges by geographical area (2006 pence) 51 Table 4.7: Car park charges at rail stations 53 Table 4.8: Estimated single journey (one-way) rail fares non-london (2007) 55 Table 4.9: Estimated Fare Fraction Paid by Traveller Category 57 Table 4.10: Weighting of time components in generalised cost 59 Table 5.1: Dimensions of population inputs 61 Table 5.2: Car availability definitions in land use model and TDM 62 Table 5.3: Source of trip attraction weights 64 Table 5.4: HBW and HBEB Trip Generation Rates: 24 hour From-Home trips per employed resident 65 Table 5.5: Education Trip Generation Rates: 12 hour From-Home trips per person in education 65 Table 5.6: Education travel proportions: 12 hour trips split by time of day and direction 66 Table 5.7: Income split by GSeC for commuters 67 Table 5.8: Proportions used for Zone Matching of tours by purpose 68 Table 6.1: Distribution parameters for Car and Public Transport, with WebTAG default values 71 Table 6.2: Distribution parameters for Walk and Cycle, with RAND illustrative values 73 Table 6.3: Relative sensitivity of main mode, time of day (TOD) and distribution choice 74 Table 6.4: Relative sensitivity of main mode and distribution, with WebTAG default values 74 Table 6.5: Sub-mode choice parameters 74 Table 6.6: Car cost damping parameters by trip purpose 76 Table 6.7: PT cost damping parameters by trip purpose 76 Table 6.8: N-hour to 1-hour vehicle trip ratios 84 Table 6.9: LGV non-commuting EB van activity 86 Table 6.10: Estimating goods delivery as proportion of LGV trips, by time period 87 Table 7.1: Commuting mode share - CSRM vs Observed Data (both 2001) 90 Table 7.2: Education mode share by school district 2006 Model vs 2007/2008 National Pupil Database 91 Table 7.3: Volumes, mean and standard deviation of trip length, inbound across RSI cordons: CSRM vs RSI by purpose, 3 hour total AM + IP + PM 95 Table 7.4: Time of Day Split (% of 12 hour trips) by Purpose for travel by car, RSI versus model. 100 Table 7.5: Comparison of Light Vehicle numbers of trip origins: LHM v10 vs CSRM synthetic matrix 102 Table 7.6: Demand model synthetic matrix car own-cost elasticity (vehicle-km with respect to fuel cost), study area residents only, by user class and time period 106 Table 7.7: Demand model synthetic matrix car own-cost elasticity (vehicle-km with respect to fuel cost), study area residents only, for HBW by socio-economic classification 106 Table 7.8: LHM matrix elasticities (vehicle-km with respect to fuel cost) all traffic originating in the Study Area, by user class and time period based on 1-hour assignment 107 Table 7.9: LHM matrix elasticities (vehicle-km with respect to fuel cost) internal to internal movements only, by user class and time period based on 1-hour assignment 107 Table 7.10: Network elasticities of vehicle-km on links in study area with respect to fuel cost, by user class and time period 108 Cambridge Sub Regional Model (CSRM) Transport Demand and Public Transport Model Development and Validation Report 6

7 Table 7.11: Demand model synthetic matrix car own-time elasticity (Car trips with respect to an increase in journey times) 109 Table 7.12: Public transport own-cost elasticity (PT trips with respect to fares) 109 Table 8.1: Survey results by site, and adjustments for missing buses and for year and season 115 Table 8.2: Comparison of numbers of passengers surveyed with passengers counts, by site and service 116 Table 8.3: Frequency of survey responses regarding activity at origin and at destination 117 Table 8.4: Potential sources of error or variation in bus passenger counts 118 Table 8.5: Number of Passengers surveyed who were either Park and Ride passengers or who were travelling to/from a Rail Station 121 Table 8.6 Passengers crossing bus screenlines: Model 2006 versus CCC Traffic Monitoring Report (March and October 2006) 126 Table 8.7: Model 2006 versus Park and Ride RSI data: number of person trips and vehicles entering/exiting each Park and Ride site by TOD 128 Table 8.8: Model 2006 data output for Babraham Road Park and Ride site 129 Table 8.9: Comparison between LATS Counts and 2006 Model Output: AM Peak only 131 Table 8.10: Comparison between LATS Count and 2006 Model Output: 12 hours 131 Table 8.11: Comparison between LATS Count and 2006 Model Output: Interpeak 131 Table 8.12: Comparison between LATS Count and 2006 Model Output: PM Peak 132 Table 8.13 Model 2006 versus CCC data: number of cyclists crossing the Outer and Inner Cordon. 133 Table F.1: Unadjusted bus survey passenger counts, by site, time period and direction 166 Table F.2: Modelled and Observed Trips by Purpose 167 Table F.3: Observed and Modelled trips by Time of Day and Direction 168 Table F.4: Observed and Modelled trips by Survey Site Figure 1-1: CSRM modelling framework 2 Figure 1-2: Input of policy options within CSRM modelling tool 3 Figure 1-3: CSRM study area 4 Figure 1-4: Iteration of the CSRM modelling tool 5 Figure 2-1: TDM linking to LU and highway assignment submodels 7 Figure 2-2: TDM transport zones 9 Figure 2-3 TDM and LU model zones within the study area 10 Figure 2-4 TDM and LU model zones within the Cambridge City 11 Figure 2-5 Public transport hierarchical design 20 Figure 2-6: Choice hierarchy for Home-Based Work and Home Based Employer s Business 22 Figure 2-7: Sub-mode tree for car main mode 23 Figure 2-8: Sub-mode tree for public transport main mode 23 Figure 3-1: TDM 2006 multi-modal network 30 Figure 3-2 Base year bus services including Park & Ride buses 34 Figure 3-3: Link connectivity for bus journeys 35 Figure 3-4: Link connectivity for rail journeys with and with no car access 37 Figure 3-5: Rail network 38 Figure 3-6 Catchment area of four major rail stations for car access 39 Figure 3-7: Link connectivity for Guided Bus journeys with no car access 40 Figure 3-8: Link connectivity for Park and Ride journeys 41 Figure 3-9 Cambridge zones without car access to bus Park and Ride sites 42 Figure 3-10 Pedestrian network linkages 43 Figure 3-11 Cycle network linkages 43 Figure 3-12: Intrazonal distances by zone (circle diameter is intrazonal distance) 45 7 Cambridge Sub Regional Model (CSRM) Transport Demand and Public Transport Model Development and Validation Report

8 Figure 4-1: Zones / areas with parking charges 52 Figure 4-2: 2007 Bus fares by distance (2007 prices) 56 Figure 4-3: Day Rider Zones (2006 Estimate) 56 Figure 4-4: Relationship between PT wait time and service frequency 58 Figure 5-1: Income profile of employed residents by GSec and car availability 63 Figure 6-1: Main levels of choice hierarchy 70 Figure 6-2: Model structure, showing relationship between the Transport Demand Model and the SATURN Local Highway Model 81 Figure 7-1: Trip volumes inbound across RSI cordons: CSRM vs RSI by purpose and time of day 93 Figure 7-2: Mean and standard deviation of car trip lengths, inbound across RSI cordons: CSRM vs RSI by purpose and time of day. 94 Figure 7-3: HBW, AM peak hour trip length, inbound across RSI cordons 96 Figure 7-4: HBW, Inter-peak hour trip length, inbound across RSI cordons 96 Figure 7-5: HBW, PM peak hour trip length, inbound across RSI cordons 96 Figure 7-6: EB, AM peak hour trip length, inbound across RSI cordons 97 Figure 7-7: EB, Inter-peak hour trip length, inbound across RSI cordons 97 Figure 7-8: EB, PM peak hour trip length, inbound across RSI cordons 97 Figure 7-9: Education, AM peak hour trip length, inbound across RSI cordons 98 Figure 7-10: Education, Inter-peak hour trip length, inbound across RSI cordons 98 Figure 7-11: Education, PM peak hour trip length, inbound across RSI cordons 98 Figure 7-12: Other/discretionary, AM peak hour trip length, inbound across RSI cordons 99 Figure 7-13: Other/discretionary, Inter-peak hour trip length, inbound across RSI cordons 99 Figure 7-14: Other/discretionary, PM peak hour trip length, inbound across RSI cordons 99 Figure 8-1 Bus Survey Locations (Cordon points marked with pink bars) 112 Figure 8-2: Stagecoach ticket sales index for selected weeks, for routes into and out of Cambridge (excluding Citi 5) 119 Figure 8-3: Stagecoach ticket sales index for Citi 5 service for selected weeks 120 Figure 8-4: Cordon Counts: Model vs Observed by Purpose (All Day) 123 Figure 8-5: Cordon Counts: Model vs Observed by TOD and Direction 124 Figure 8-6: Cordon Counts: Model vs Observed by Site 125 Figure 8-7: Map of Study Area Rail Network 132 Figure 8-8 Inner Cordon cycle monitoring point locations (2007) 134 Figure 8-9 Outer Cordon monitoring point locations (2007) 135 Figure E-1 AM Peak HBW Trips by Mode 159 Figure E-2 Inter Peak HBW Trip Lengths by Mode 159 Figure E-3 PM Peak HBW Trip Lengths by Mode 160 Figure E-4 AM Peak EB Trip Lengths by Mode 160 Figure E-5 Inter Peak EB Trip Lengths by Mode 161 Figure E-6 PM Peak EB Trip Lengths by Mode 161 Figure E-7 AM Peak Other Trip Lengths by Mode 162 Figure E-8 Inter Peak Other Trip Lengths by Mode 162 Figure E-9 PM Peak Other Trip Lengths by Mode 163 Figure E-10 AM Peak Education Trip Lengths by Mode 163 Figure E-11 Inter Peak Education Trip Lengths by Mode 164 Figure E-12 PM Peak Education Trip Lengths by Mode 164 Cambridge Sub Regional Model (CSRM) Transport Demand and Public Transport Model Development and Validation Report 8

9 1 Introduction : 1&; 089# The CHUMMS model for the A14 Cambridge to Huntingdon corridor was developed in 2000 as one of the multi-modal studies undertaken by government to look at highway infrastructure improvements in key corridors throughout the country and supporting transport interventions The CHUMMS model comprises an integrated land use and transport demand model representing all passenger modes of transport. Following the multi-modal study, the transport model was developed further to allow it to be used for the Highways Agency s Targeted Programme of Improvements (TPI) initiative. Subsequently a more detailed and local highway model was developed making use of the latest traffic data to improve the representation of the A14 corridor to provide the Highways Agency with the detailed traffic forecast information to progress the A14 scheme proposals to public consultation In 2006 work commenced on a model to be used for the Cambridgeshire Transport Innovation Fund (TIF) bid. The agreed approach was for WSP to update the CHUMMS model and for this to be combined with an updated SATURN Local Highway Model (LHM) to be supplied by Atkins Since then the two modelling teams (WSP and Atkins) have worked together to produce a revised integrated modelling system known as the Cambridge Sub Regional Model (CSRM) for use by CCC The model development has been sponsored by CCC. The full modelling system is compliant with the current DfT guidance on demand modelling, particularly the requirements for modelling road pricing relevant for the TIF work. : & #& The CSRM allows stand-alone testing of road, PT, cycle, walk schemes, standard economic benefit tests using the highway and demand model with fixed trip ends, as well as complex tests of strategic policy options incorporating land use responses The essential features of the CSRM model structure, shown in Figure 1-1 are: A linked land use model to generate trip ends from forecast planning data and travel accessibilities. A detailed Transport Demand Model (TDM), as described in this report, using WSP s MEPLAN software. It includes traveller responses including choice of mode/submode, change of (macro) time day of travel, and trip redistribution among destinations. Travellers are segmented by income, trip purpose and car ownership. This model is compliant with current Department for Transport guidance for variable demand modelling including the assessment of road pricing schemes. A public transport, walk and cycle assignment submodel (PT-Walk-Cycle) also implemented in MEPLAN. A highway assignment submodel (LHM) using Atkins SATURN software for light and heavy good vehicle (HGV) assignment, described in detail in a separate report CSRM Model Development Report 1

10 Previous Year Scenario/Policy Year MENTOR Land Use Starting Costs Transport Demand Public Transport Assignment P&R car legs SATURN HW Demand Public Transport Assignment Growth Factors X Integrated Transport Model iterates to convergence SATURN Highway Model SATURN Highway Model Integrated Transport Model Figure 1-1: CSRM modelling framework In addition to road-pricing, highway infrastructure and public transport schemes, the CSRM modelling tool is designed to address effectively a variety of policy options, as represented by Figure 1-2 below The demand model does not include demand responses for Heavy Goods Vehicles other than their route choice within the LHM, see Section 6.14 for details. 2 CSRM Model Development Report

11 Input of Policy Options Model Outputs Policy Measures Employment, Commercial Development and Housing Scenarios Public Transport, Walk, Cycle Schemes Road User Charging Land Use Transport Trip Costs Ends Transport Demand Transport Trip Matrix Costs by by mode mode Public Transport Assignment Highway Highway Growth and Trip Costs Re-distribution Re-location of households and employment Future transport patterns, destinations of leisure/retail trips Take up of each mode, by origin and destination PT, Walk, Cycle route Choice. Average distance, costs, times for Highway, and PT, Walk and Cycle Housing and Commercial Floorspace Attractiveness by Location Social and Skills mix by District Balance of In and Out Commuting Fit with National and Regional Policy Goals Wider Economic Impacts Accessibility Impact of Transport Policy and Take-up of Alternative Travel Modes Carbon Emissions by location, journey type etc. Highway Improvements Local Highway Model SATURN Journey Times, Congestion. Light and HGV route Choice Traffic Deaths and Injuries (based on passenger km by type) Congestion Levels, time spent in traffic, road speeds Figure 1-2: Input of policy options within CSRM modelling tool This report provides an overview of the design, development and capabilities of CSRM Transport Demand Model (TDM), as well as the PT-Walk-Cycle assignment submodel. The land use (LU) model and the SATURN highway assignment model (LHM) are described in more detail in separate reports. :" 94&5& The choice of study area boundary and subdivision of this geographic area into model zones is key to the geographic resolution and forecasting capabilities of the model. The chosen study area is defined by the local authority districts which closely match the definition of the Cambridge Sub Region The four districts making up the CSRM model study area are: Cambridge, South Cambridgeshire, East Cambridgeshire, Huntingdonshire Figure 1-3 shows the CSRM study area with the rest of Great Britain outside the study area represented as an external area. The external area is used to represent the exchange of trips with the internal area, and the through trips traversing the internal study area with both ends of the trips in the external area. The study area boundaries are consistently represented throughout the modelling system i.e. in the LU, TDM, PTwalk-cycle assignment and LHM CSRM Model Development Report 3

12 Figure 1-3: CSRM study area :( 855< 86=58533#06&5< 8; The modelling methodology for CSRM incorporates all components of the traditional four-stage model: trip generation, choice models for mode/submode, time of day and distribution; and assignment of modal trips to routes The CSRM modelling tool consists of a SATURN highway submodel (LHM), a MENTOR based land use model (LU) and a Transport Demand Model (TDM), and PT- Walk-Cycle assignment submodel (see Figure 1-4) implemented in MEPLAN. Cost and trip matrices are exchanged between all the components: SATURN, TDM, and LU model. The LU model does its own demand modelling to support the land use allocation in producing the trip ends for the demand model. 4 CSRM Model Development Report

13 1.4.3 The land use (LU) model is implemented for a starting year of 2001 to make use of the Census data available and then updated to a Base year of The transport model base year is an average 2006 weekday. The model is then run through time to produce forecasts for the five years intervals between 2011 and Each future year builds on the results from the previous years. Previous Year Iteration 1 LU (trip ends) Iteration 2 Costs Demand Starting Costs Transport Demand Transport Demand PT-Walk-Cycle Assignment PT-Walk-Cycle Assignment PT-Walk-Cycle Assignment P&R car legs Growth Factors Growth Factors Convergence Test SATURN HW Demand X X SATURN Highway Model SATURN Highway Model SATURN Highway Model Figure 1-4: Iteration of the CSRM modelling tool The Land Use model produces population and employment data which are used to generate average weekday trip ends (productions and attractions) segmented by trip purpose, household car availability and by socio-economic group. The land use model provides information for all trips with at least one trip-end within the study area and combines a wide variety of socio-economic, land use and transport data into a consistent, spatial framework with changes in transport accessibility influencing location choices for both population and businesses The transport demand model aggregates the daily person trip ends from the land use model into fewer trip purposes and household car availability segments and carries out the trip distribution by time of day and main mode Both the PT-Walk-Cycle and LHM assignment submodels are implemented for three time segments: the PT-Walk-Cycle assignment model uses morning peak from 07:00 to 10:00, the interpeak period from 10:00 to 16:00 hours, and the evening peak from 16:00 to 19:00; whereas the highway assignment is implemented for weekday peak and interpeak hours from 08:00 to 09:00, 14:00 to 15:00, and 17:00-18:00. The PT- Walk-Cycle assignment model covers all relevant bus, rail and future Guided Bus services in the Cambridge sub-region. Crowding or overcrowding are not currently modelled CSRM Model Development Report 5

14 1.4.7 Matrices of through trips for light vehicles (external to external movements) are obtained from the East of England Regional Model (EERM). Also all the heavy goods vehicle trips including internal trips are added to the highway assignment model as exogenously defined input matrices By comparing policy runs of the TDM with the base year (2006) results, growth factors are produced which can be used to scale the LHM matrices from the previous period or iteration The LU model forecasts future locations of residence, employment and retail activities, and from these derives trip ends. The trip matrices produced within the TDM will be influenced by a wide-range of factors including: Transport costs and disutilities in previous run years; Changes in land use (new dwellings and commercial floorspace available); Changes in employment, which are both input as exogenous assumptions and predicted by the model itself. :* 99586= This report provides an overview of the design, development and capabilities of CSRM Transport Demand Model (TDM); the LU model and the highway assignment model (LHM) are described separately. It aims to inform model users about the functionality of the completed model, its strengths and weaknesses Chapter 2 provides an overview of the TDM, its structure and definitions, and the zoning upon which model is defined geographically Chapter 3 covers the design and functionality of the multi-modal networks with the focus on PT, cycle and walk networks. This section reviews the approach to path building and assignment that is used to provide the supply characteristics for the demand model. A separate report by Atkins provides the detailed description of highways networks and assignment implemented in the LHM Chapter 4 provides information on the input characteristics to generalised cost formulation. Those which are input to the model and the derivation of those determined iteratively within the model Chapter 5 provides an overview of the travel demand inputs such as planning data inputs and trip rates Chapter 6 provides a description of the model implementation, including its theoretical structure and the link between the demand and LHM models Chapter 7 provides demand model validation comparisons for trip lengths, car trips, time of day and mode choice and includes a description of the realism tests undertaken and results obtained. Chapter 8 shows the PT validation statistics. 6 CSRM Model Development Report

15 2 Transport Demand Model (TDM) Structure and Definition : 855< 86= The TDM has been designed to: Represent the passenger movements between model zones; Represent the choice between different transport modes, between different time periods, and between different destination zones; Represent the route choice and path assignment of public transport including Park and Ride, walk and cycle trips. Route choice for heavy and light vehicles is implemented within the SATURN LHM The diagram below shows the structure of the TDM linking to the other submodels within the CSRM Model. Trip ends LU model: Trip-ends Mode choice Composite generalized costs for travel purposes TDM (MEPLAN) Time of Day choice Destination choice Trip matrix by mode: car, PT, walk, cycle Generalized costs all modes Sub-mode choice and PT, walk, cycle assignment. (MEPLAN) PT/Walk/Cycle network assignment Generalized costs for light vehicles Synthetic light vehicle trip matrix Matrix pivoting (SATURN) LHM light and HGV assignment Forecast highway matrix Figure 2-1: TDM linking to LU and highway assignment submodels The TDM is used to estimate: travellers choice of main travel modes: car, PT, walk and cycle, the split of journeys between different time periods of the day: AM, IP, PM and off peak time periods, out and return journeys between where travel is produced and where it is attracted to CSRM Model Development Report 7

16 Travellers choice of submode of travel (bus, rail/guided Bus, car versus car Park and Ride) The TDM is fully integrated with the two network submodels used for the path building / assignment on the networks for each of the following modes: car, Park and Ride, bus (Guided Bus in future model years), rail, cycle and walk. The MEPLAN assignment model is used for all of these modes, except for light vehicles (mainly cars plus LGVs) and HGVs which are assigned using SATURN Matrices of congested journey distances, costs and times by travel purpose and income level (SATURN User Class) from the local highway model for the three assigned one hour peak periods are read directly into the TDM. Generalised costs between each relevant origin and destination pair for each mode for each time period modelled are constructed from these matrices by the demand model for the different traveller types using appropriate values of time The composite costs are passed consistently up the choice hierarchy from the network level using the conventional log-sum formulation in hierarchical logit models of discrete choice. The travel costs from the equilibrated TDM are fed into a linked land use model which provides the planning data to generate trip ends and constraints for the TDM The travel demand and network models iterate until a converged equilibrium solution is achieved for travel demand and congestion estimation. : 28## There are 306 transport model zones within the TDM as shown in Figure 2-2 below. The locations outside the study area are represented as a set of larger external zones. This external area is used to represent the exchange of trips with the internal area, and the through trips traversing the internal study area The zoning systems for the various components of the modelling system are consistent but not exactly the same. The entire zoning system relates to the ward or Output Area boundaries for which the 2001 Census data is available The CSRM zoning systems nest as follows: LU zones are made up of whole Wards, or groups of Output Areas within Wards, comprising the study area s four districts. TDM zones nesting within LU Zones (see Figure 2-3, and Figure 2-4 for the correspondence between the zones in the TDM and LU within the study area and Cambridge city respectively). LHM zones are mostly the same as the TDM zoning. A few TDM zones are further subdivided to improve highway assignment, including explicit zones for the Park and Ride sites. The TDM builds public transport paths to the Park & Ride sites without needing separate zones for them. 8 CSRM Model Development Report

17 Figure 2-2: TDM transport zones CSRM Model Development Report 9

18 Figure 2-3 TDM and LU model zones within the study area 10 CSRM Model Development Report

19 Figure 2-4 TDM and LU model zones within the Cambridge City Table B.1 in the Appendix B gives a correspondence between the TDM, the other CSRM submodels, wards, and districts. Table 2.1 below provides a summary of the zones within the four districts of the study area. Table 2.1: Number of zones within the model by district Study area district / External area number of LU zones number of TDM zones number of LHM zones Cambridge South Cambridgeshire East Cambridgeshire Huntingdonshire Total Internal External Total CSRM Model Development Report 11

20 :" 5#8#&#9# The model is implemented for a starting year of 2001 making use of the Census data, and then updated to a Base year of 2006 for consistency with the local highway model validation. The model is then run through time to produce forecasts for 2011, 2016, 2021, 2026 and All cost related inputs and outputs are in 2006 prices The TDM and assignment submodels have been implemented in the following dimensions and units. Table 2.2: Dimensions and units Dimension Units TDM and PT-Walk-Cycle LHM assignment assignment Distance Kilometres Metres Time Minutes Seconds Cost / Money Pence (in 2006 prices) Pence (in 2006 prices) Speed Kilometres per hour Kilometres per hour Disutility Generalised minutes: combination of time and costs Generalised seconds: combination of time and costs The unit of demand for travel for all home-based (HB) travel within the TDM is a simple tour: that is for HB work, HB education, HB employer business, and HB other. A simple tour is defined as a round trip starting and finishing at the traveller s home with one destination Non-home based (NHB) movements are separated into two distinct purposes: Employer's Business and Other defined as all remaining purpose categories combined. NHB movements are modelled as trips comprising a single leg from the origin to the destination. They are generated from HB trip ends (eg. Persons arriving at work) using an approach similar to that embedded with the National Trip End Model (NTEM, DfT, 2008). :( 5&#505#&8# The segmentation of traveller demand in the TDM, and assignment submodels has been designed in a way to capture the differences in behavioural responses relevant to the choices being modelled. Good segmentation is important for different groups within the model to be able to respond realistically, but additional segmentation also imposes a computational cost. The design is based on a balance between realism and practicality, using a combination of past experience, research, and TAG guidance There are a maximum of five home-based (HB) and 2 non-home-based (NHB) journey purposes represented within the TDM as shown in Table CSRM Model Development Report

21 Table 2.3: Trip purposes in TDM Abbreviation Purpose Value of time category HBW Home-Based Work Non-work non-discretionary HBEd Home-Based Education Non-work non-discretionary HBPS HBRV Home-Based Personal business and Shopping Home-Based Recreation and Visiting friends & relatives Non-work discretionary Non-work discretionary NHBO Non-Home-Based Other Non-work discretionary HBEB Home-Based Employer s Business Work NHBEB Non-Home-Based Employer s Business Work These 7 purposes are consistent with the National Transport Model (DfT, 2003, NTM) and an aggregation of those in TEMPRO; and allow quite different attraction locations, trip lengths and time period profiles to be taken into account Table 2.4 shows how the trip purposes from the TDM are aggregated for use in both the PT-Walk-Cycle and the LHM assignment models. Table 2.4: Purpose segmentation in each submodel TDM trip purpose PT-Walk-Cycle assignment submodel Highway assignment submodel (LHM) HBW HBW HBW HBEd HBEd HBEd HBPS HBRV NHBO HBEB NHBEB Other EB Other EB Travel purposes are further segmented in various ways depending on the trip purpose. The segmentation also varies at different stages of the model depending upon what choices have been made higher up the hierarchy, and what is important for modelling behaviour. For example, household car availability is very important in mode choice but less important in distribution, so it is dropped at lower levels of the hierarchy to reduce the size of the model The following segmentation definitions are adopted for different choice stages and travel purposes of the TDM CSRM Model Development Report 13

22 Car availability: the following classification of households that combines information on number of cars with that on number of adults within the household was used: None: households that do not have a car available for private use, Part availability: households of two or more adults having only one car available for private use, Full availability: one adult household with one car, or two or more adults with two or more cars available for private use. Employment status: whether the person is in: Full-time work (31 hours or more per week) or, Part-time work (30 hours or less per week) Socio-economic classification, which represents differences in occupation and economic status. TDM uses an aggregation of the standard National Statistics socioeconomic classification (NS-SeC), called Grouped Socio-economic Classification (GSeC) as defined in Table 2.5. GSeC is somewhat correlated with income. However, for commuting travel, GSeC is better able to capture the variation in behaviour (especially trip length and different location of jobs) than income and data is available from the Census to assist in calibrating the model. For this reason in CSRM the travel to work (HBW) tour distribution stage is segmented by the four employed GSeC categories rather than by income. This demand is then segmented by income for the sub-mode choice and assignment stages, as described in more detail below. Income band: based on gross annual household income in 2006 prices: Low income (< 17,500). Medium income ( 17,500 to 35,000) High income (> 35,000) Time of Day (TOD): four time periods within the average weekday: AM peak: 07:00 09:59, Inter peak : 10:00 15:59, PM peak: 16:00 18:59, and Off Peak: 00:00 06:59 and 19:00 23:59. The off-peak is only modelled as a sink (volume is allocated but not assigned in the demand choice models) and only for HBW and EB trips. Main mode choice: Car / light vehicle, Public Transport, Walk and Cycle. See Table 2.10:. Age group of the pupil for education trips: Primary: age 5-11, Secondary: to fit with employment age in Census of 16 and over, Sixth form: Age 16-18, Tertiary/adult: Age 19 and over. 14 CSRM Model Development Report

23 2.4.7 Note that age is not necessarily the same as curriculum year. In particular, students typically move from a primary school to a secondary school somewhere around the age of rather than on their birthday. This means that the Primary segment may actually include some secondary school students and vice-versa. This can also be true of few students moving to a separate 6 th form college at the age of This age segmentation is necessary because the model is based on the population Census at the home end, which is tabulated by age. Fortunately the proportion of students affected is small, and the model should still be superior to models which combine all school years into a single segment. Observed school data by age and curriculum year has been used to adjust school size terms in the model appropriately. With the exception of Independent Schools the data used was taken from the 2007 National Pupil Database (NPD) supplied for the Study Area by the Department for Children, Families and Schools (DCFS). The data for the Independent Schools were obtained from Cambridgeshire County Council Table 2.6 to Table 2.9 below give the segmentation of the TDM travel demand at the main mode, time of day, tour distribution, submode and route choice stages for each of the trip purposes modelled. Table 2.5: Grouped Socio-economic Category (GSeC) defined from NS-SeC Grouped Socioeconomic Category (GSeC) GSeC1 GSeC2 GSeC3 GSeC4 GSeC5 GSeC6 National Statistics Socio-economic Classification (NS-SeC) as used in 2001 Census 1.1 Large employers and higher managerial occupations 1.2 Higher professional occupations 2. Lower managerial and professional occupations 3. Intermediate occupations 4. Small employers and own account workers 5. Lower supervisory and technical occupations 6. Semi-routine occupations : 7. Routine occupations 8. Never worked and long-term unemployed - Persons over retirement age not in employment CSRM Model Development Report 15

24 Table 2.6: HBW trips segmentation by choice stage Choice Stage Number of segments Dimension A Dimension B Dimension C Dimension D Main mode 24 Car availability[3] GSeC[4] F-/P-time[2] TOD 32 Main mode[4] GSeC[4] F-/P-time[2] Distribution, car vs P&R 128 TOD[4] Main mode [4] GSeC[4] F-/P-time[2] PT Submode 9 TOD[3] Income[3] Assignment: car legs 9 TOD[3] Income[3] Assignment: PT legs 12 TOD[3] Sub-mode[4] (bus,rail,gb, P&R) Attraction constraints 8 GSeC[4] F-/P-time[2] Table 2.7: HBEd trips segmentation by choice stage Choice Stage Number of segments Dimension A Dimension B Dimension C Dimension D Main mode 36 Age[4] Income[3] Car availability[3] Distribution, car vs P&R PT submode Assignment: car legs Assignment: PT legs Attraction constraints 48 Age[4] Income[3] Main mode[4] 36 Age[4] Income[3] TOD[3] 3 TOD[3] 12 TOD[3] Sub-mode[4] (bus,rail,gb, P&R) 4 Age[4] 16 CSRM Model Development Report

25 Table 2.8 HB and NHB EB trips segmentation by choice stage HB / NHB trips Choice Stage Number of segments Dimension A Dimension B Dimension C HBEB Main mode 9 Car availability[3] TOD 12 Main mode[4] Income [3] Income[3] Distribution, car vs P&R 48 TOD[4] Main mode[4] Income[3] NHBEB Distribution, car vs P&R 36 TOD[3] Main mode[4] Income[3] HB & NHB combined PT submode 9 TOD[3] Income[3] Assignment: car legs 3 TOD[3] Income[1] Assignment: PT legs 12 TOD[3] Sub-mode[4] (bus,rail,gb, P&R) CSRM Model Development Report 17

26 Table 2.9: Discretionary (HBPS, HBRV and NHBO) trips segmentation by choice stage Purpose Choice Stage Number of segments Dimension A Dimension B Dimension C HBPS Main mode 9 Car availability[3] TOD 12 Main mode[4] Income [3] Income[3] Distribution 36 TOD[3] Main mode[4] Income[3] HBRV (same as HBPS) Main mode 9 Car availability[3] TOD 12 Main mode[4] Income [3] Income[3] Distribution 36 TOD[3] Main mode[4] NHBO Distribution 36 TOD[3] Main mode[4] Income[3] Income[3] All discretionary HB & NHB combined PT Submode Assignment: car legs Assignment: PT legs 9 TOD[3] Income[3] 9 TOD[3] Income[3] 9 TOD[3] Sub-mode[4] (bus,rail,gb, P&R) Below the tour distribution choice stage for commuting travel, each GSeC and Full/Part time combination is subdivided into the percentage in each of the three income groups using National Travel Survey (NTS) data 2006 to provide the proportional split by income group for each GSeC category. The results are then aggregated into income categories, prior to carrying out the submode choice and the car assignment for incomebased user classes: Low (less than 17,500); Medium (from 17,500 to 35,000), and High (higher than 35,000) of annual salaries in 2006 prices Commuting tours are generated at the residence (production) end and are constrained at the workplace (attraction) end. The trip ends at the residence zones are segmented by the 24 categories listed at the main mode choice stage in Table 2.6 above HB education tours are generated at the residence (production) end and are constrained at the school/college (attraction) end, except for tertiary/adult tours, for which there was insufficient data to doubly constrain the distribution choice. The trip productions at the residence zones are segmented by the 36 categories listed at the main mode choice stage Table CSRM Model Development Report

27 Unlike the home-based commuting and education travel purposes, there is no comprehensive source of local data describing modal travel patterns on home based employer s business or for discretionary travel. Hence, these trip purposes are represented as singly constrained tours within the model Non-home based movements are represented for two distinct purposes: employer s business and other, which is defined as all remaining purpose categories combined. They are modelled as singly constrained trips not tours. No explicit time of day (TOD) choice or main mode choice stage is included here. Instead, non-home based trips are generated using fixed trip rates by time period and main mode, which are applied only to those zonal attractions that have already arrived in the previous or current time period. :* 8556#8# The main modes for mode choice are car, PT, walk and cycle. Car further subdivides into car and Park & Ride (car plus bus/guided Bus); PT into bus and rail/gb, while rail further divides into rail (this being either with car or with no car access), and GB. (See Figure 2-8: Sub-mode tree for public transport main mode) The path building and assignment is carried out for each of the following individual modes: car, Park and Ride, bus, Guided Bus, rail, cycle and walk. Path building uses the PT-Walk-Cycle assignment submodel (MEPLAN based software) for all of these modes, except for car and the car legs of P&R and rail trips, which are assigned in the SATURN based LHM The main modes of travel defined in the TDM are shown in the table below The modes shown are defined as the main mode of travel. Travellers using public transport main modes can access that mode by another access mode. For example driving to the station or walking to the bus stop. The combinations of access modes allowed for each main mode of travel have been defined to ensure representation of realistic travelling patterns CSRM Model Development Report 19

28 Table 2.10: Transport modes in the CSRM model Main mode Sub-mode Description Car / light vehicles Car Park & Ride (P&R) Both car drivers and passengers. Includes cars, motorcycles, taxis and other light vehicles (vans) used for personal travel* (Work / business car is differentiated from non-work car). Car plus either bus or Guided Bus at current or future Park & Ride sites Bus Bus Local and regional bus services Rail, drive to station Timetabled services from, to and within study area Rail, no car leg Rail New mode between St Ives and Cambridge represented Guided Bus in forecasting years only. Walk Journeys entirely on foot. Cycle Journeys entirely by bicycle. Note: *The demand model trip rates have taken account of LGV movements in the National Travel Survey (especially for HBW and Employer s Business) and so the TDM s travel demand includes passenger or EB trips. The NTS however does not include trips primarily for goods delivery or delivery rounds. See Section 6.14 regarding Goods Vehicle demand modelling Public transport definitions conform to a hierarchical design, as shown in the diagram below. Where a trip includes a rail element, it is a rail trip. If a trip includes a Guided Bus element and no rail element, it is a Guided Bus trip. If a trip includes a Guided Bus element and a bus element, it is a Guided Bus trip. Trip in this case is defined by the overarching user mode. Figure 2-5 Public transport hierarchical design Park and Ride trips are those that use bus, Guided Bus or both with a car drive leg of the journey at one end. Park and Ride cannot use rail services (interchange is prohibited). This is partly related to the hierarchical design issue, and also to the assumption that bus-based Park and Ride is not used to access rail. A passenger who walks to a Park and Ride site and catches the bus is making a bus trip and not a park and trip, even though it is a Park and Ride branded bus, and similarly if they walk to a Guided Bus site they are making a Guided Bus trip without car access. 20 CSRM Model Development Report

29 Table 2.11: Access Modes available for Main Modes Permitted Access Modes PT Main Mode Car Walk Cycle Bus Guided Bus Rail Y Y Y Y Y Guided Bus - Y Y* Y - Bus - Y Y* - - P+R Y Note: * a cyclist can access Bus and Guided Bus only at designated sites such as Park and Ride sites. Y =YES. :+ = The set of traveller choices represented in the TDM and their relative importance in the choice hierarchy is constructed in accordance with the DfT s WebTAG Unit Section 1.9 (DfT, June 2006) where appropriate local data is not available to determine the model structure. TAG unit defines the following traveller s choices and their relative sensitivity to travel cost and time differences, from least to most sensitive: Trip frequency may be represented as inelastic if all modes of transport including walking and cycling are considered; Main mode choice (i.e. between car, PT, walking and cycling) is as sensitive as macro time of day choice; in turn, macro time of day choice is less sensitive than micro time of day choice; Both main mode and macro time of day choices are significantly less sensitive than destination choice; Destination choice in turn is less sensitive than sub-mode choice, i.e. between car and park-and-ride, or between bus, and rail, GB services; Micro time of day choice for peak spreading on road is more sensitive than sub-mode choice. Finally, route choice among alternative paths in the network appears most sensitive of all of the choices, i.e. to travel costs and times of the options available For each travel purpose the TDM assumes a fixed trip frequency by traveller type since all modes including walk and cycle are represented in the model. The TDM then maintains the following choice sequence, from top (least sensitive) to bottom (most sensitive): Main mode choice: car versus public transport (PT) versus walk versus cycle. Macro time period choice for the outbound leg of modal tours for commuting, employer s business and discretionary travel. Education has no time of day choice. Tour distribution individually for each combination of main mode and outward time period CSRM Model Development Report 21

30 Sub-mode choice: car versus Park and Ride; bus versus rail this followed by rail versus Guided Bus this followed by rail with no car access versus park at rail station. PT-Walk-Cycle route choice. Light and HGV assignment is implemented in LHM A summary of the choices modelled with an indication of the model (i.e. module) used within the CSRM is presented in the table below: Table 2.12: Summary of choices modelled within the CSRM Demand response Mode Submode choice Time of day choice Trip destination / attraction choice PT, walk, cycle route choice Highway light and HGV route choice Implemented in Module TDM TDM TDM PT-Walk-Cycle assignment LHM assignment The choice hierarchy adopted for all home based commuting and business trips is shown in more detail in Figure 2-6. The hierarchy for HB Education is the same except that there is no time of day choice, and for home based discretionary trips (HBPS and HBRV) it is the same except that there is no Off-peak period. For space reasons the hierarchy in Figure 2-6 has been expanded only on the car/p&r mode branch, but time-of-day and destination choices are the same for the other main mode choices. These branches are further expanded in to Figure 2-7 and Figure 2-8 below. Walk and cycle have no sub-mode split. Trip generation Mode car/park and Ride public transport walk cycle AM peak Interpeak PM peak Off-peak (night) Destination 1 Destination 1 Sub-mode car Sub-mode Park & Ride Destination 2 Figure 2-6: Choice hierarchy for Home-Based Work and Home Based Employer s Business 22 CSRM Model Development Report

31 Car tour A-B Car Park & Ride Site: Trumpington Site: Newmarket Rd. Site:... Figure 2-7: Sub-mode tree for car main mode PT tour A-B Bus Rail/GB Guided Bus (not P&R) Rail Figure 2-8: Sub-mode tree for public transport main mode Rail, no car leg Rail, drive to Cambridge Rail, drive to Huntingdon Rail, drive to St. Neot s Rail, drive to Ely stations Rail, drive to local station Both main mode and macro time of day choices are less sensitive than destination choice, and the destination choice in turn is less sensitive than sub-mode choice, i.e. between car and bus based park-and-ride, or between bus and rail/gb. The Rail/GB mode as shown in Figure 2-8 further subdivides into Rail versus Guided Bus. Rail is split further into six submodes: Rail with no car access and Rail with parking at either Cambridge, Huntingdon, St Neots, Ely or a predefined local station The Park and Ride submode (Figure 2-7) comprises a car leg plus a bus leg from one of the five Park and Ride sites in Cambridge in 2006 (Trumpington Road, Babraham Road, Madingley Road, Cowley Road and Newmarket Road) to destination zones. It also includes future bus or Guided Bus Park and Ride sites, which includes modelling the closure of the Cowley Road site and opening of the Milton Road site as its replacement from 2008 (2011 in the model). The proportional choice of each individual site for a specific zone to zone movement is calculated using a logit choice between the set of sites. As shown on figures above, this is a further level down within the travel demand choice hierarchy for the sub-mode Park and Ride The Rail component models the ability to: drive to one of the four major rail stations within the study area (Cambridge, Huntingdon, St. Neot s, Ely) and park before catching a rail service. drive to a local (nearest) station, park and catch a rail service. The definition of local is pre-determined and input to the model for each zone CSRM Model Development Report 23

32 access a station with no car leg, with the option of walking, using a bus, or a Guided Bus to get there. In this case the choice of station is part of the route choice The distances, costs and times of car legs in P&R and rail movements are taken from skims by user class output from the LHM s congested SATURN assignment, whereas the characteristics of the PT legs are taken from the PT-Walk-Cycle network submodel As noted previously the main choice stages are carried out for the HB tours. Prior to assignment to the network these tours are converted into the trips to and from home. The details of the travel demand unit (tour, trips) for each of the choice stages for each travel purpose are specified in Table 2.13 below. Table 2.13: TDM demand units Purpose Main mode TOD Distribution & Car Submode PT Submode Assignment light and HGV Assignment PT, Walk, Cycle HBW Tour (PA) Tour (PA) Tour (PA) Trip (OD) Trip (OD) Trip (OD) HBEd Tour (PA) Tour (PA) Tour (PA) Trip (OD) Trip (OD) Trip (OD) HBPS Tour (PA) Tour (PA) Tour (PA) Trip (OD) Trip (OD) Trip (OD) HBRV Tour (PA) Tour (PA) Tour (PA) NHBO No choice generate by mode and TOD Trip (OD) HBEB Tour (PA) Tour (PA) Tour (PA) Trip (OD) Trip (OD) Trip (OD) NHBEB No choice generate by mode and TOD Trip (OD) All the choices that are labelled PA in Table 2.13 are based on the combined characteristics of the outward plus the return leg of the tour. This includes the sub-mode choice of car versus Park and Ride so as to guarantee the use of the identical mode for the out and return leg of the tour. This is not the case for public transport sub-mode split as there is no behavioural reason a traveller should not choose a different mode out and back, if the level of service differs by period. :! Different time periods are used at different stages in the model. The time periods covered by each of the sub-models are shown in the table below. There are three main sets of time periods defined: The day (12 or 24 hour) for trip generation, mode choice and input to time of day choice The 3 or 6 hour time periods output from the time of day choice used for distribution and the PT-walk-cycle assignment The 3 peak hours used for the highway assignment The definitions used at each stage are shown in Table 2.14 below. 24 CSRM Model Development Report

33 Table 2.14: Time periods represented by each model component Stage HBW and HBEB HB discretionary (HBPB and HBRV) Trip generation Weekday 24 hours Weekday 12 hours ( ) Main mode Weekday 24 hours Weekday 12 hours ( ) HB Education Weekday 12 hours ( ) Weekday 12 hours ( ) Time of day (TOD) AM ( ) IP( ) PM ( ) OP ( ) Distribution, AM ( ) IP ( ) PM ( ) AM ( ) IP( ) PM ( ) AM ( ) IP ( ) PM ( ) Not applicable Weekday 12 hours ( ) Sub-mode, PT-Walk-Cycle assignment AM ( ) IP ( ) PM ( ) AM ( ) IP ( ) PM ( ) AM ( ) IP ( ) PM ( ) Highway assignment (LHM) AM hour ( ) IP hour ( ) PM hour ( ) AM hour ( ) IP hour ( ) PM hour ( ) AM hour ( ) IP hour ( ) PM hour ( ) The characteristics by time period which are used to generate the cost of travel for a tour starting in each period are shown in Table These time periods were selected based on analysis of NTS data shown in Table 2.16 to identify which was the dominant return time period for each outward time and mode. Although the table shows some characteristics were taken from the off-peak period, this period is not typically run. The derivation of these characteristics is explained in paragraph below CSRM Model Development Report 25

34 Table 2.15: Relationship between costs by time of travel and tour cost Purpose Tour Outward leg (from home) Return leg (to home) used for tour cost HBW 1 AM peak PM peak 2 Inter peak PM peak 3 PM peak PT: Off peak Other modes: PM peak 4 Off peak Off peak HBEd (no TOD choice) 1 AM peak Inter peak HBEB 1 AM peak PM peak 2 Inter peak PM peak 3 PM peak Off peak HB Discretionary HBPS HBRV 4 Off peak Off peak 1 AM peak Inter peak 2 Inter peak Inter peak 3 PM peak PM peak 1 AM peak Inter peak 2 Inter peak Inter peak 3 PM peak Off peak The proportions in Table 2.16 were originally derived from the NTS, but for the car mode some proportions have been adjusted to better match car volumes in Road Side Interview data by time period Since the CSRM does not model HBPS or HBRV trips in the Off Peak there are no OP From Home return trip breakdowns for these purposes. 26 CSRM Model Development Report

35 Purpose HBW HBEB HBPB / Shop HB Rec/Visit Table 2.16: Return trips given time of outbound trip Main to Time period to home Period home for from home Main mode supply AM IP PM OP AM Car PM 4.8% 18.9% 69.0% 7.3% PT PM 0.4% 11.1% 70.1% 18.5% Slow PM 3.3% 38.7% 53.1% 4.9% IP Car PM 0.0% 27.2% 47.7% 25.1% PT PM 0.0% 14.3% 43.2% 42.4% Slow PM 0.0% 34.3% 51.0% 14.6% PM Car PM 0.0% 0.0% 55.7% 44.3% PT OP 0.0% 0.0% 6.1% 93.9% Slow OP 0.0% 0.0% 24.3% 75.7% OP Car OP 11.1% 30.4% 26.9% 31.6% PT IP 16.6% 41.1% 29.5% 12.7% Slow IP 21.6% 42.7% 20.4% 15.3% AM Car PM 5.1% 43.0% 49.0% 2.9% PT PM 0.8% 24.2% 48.9% 26.1% Slow IP 29.0% 44.0% 25.7% 1.3% IP Car PM 0.0% 44.8% 47.9% 7.3% PT PM 0.0% 19.3% 49.1% 31.6% Slow IP 0.0% 69.5% 29.2% 1.4% PM Car OP 0.0% 0.0% 40.9% 59.1% PT OP 0.0% 0.0% 11.1% 88.9% Slow PM 0.0% 0.0% 58.1% 41.9% OP Car OP 12.4% 9.2% 9.9% 68.4% PT OP 0.0% 10.5% 0.0% 89.5% Slow OP 25.6% 3.8% 2.6% 67.9% AM Car IP 23.5% 59.8% 14.9% 1.8% PT IP 3.4% 85.6% 10.3% 0.8% Slow IP 47.3% 49.2% 3.0% 0.4% IP Car IP 0.0% 76.0% 22.8% 1.3% PT IP 0.0% 74.3% 23.7% 1.9% Slow IP 0.0% 89.1% 10.6% 0.2% PM Car PM 0.0% 0.0% 67.8% 32.2% PT PM 0.0% 0.0% 65.0% 35.0% Slow PM 0.0% 0.0% 89.9% 10.1% AM Car IP 18.3% 56.6% 18.9% 6.2% PT IP 3.8% 64.3% 23.6% 8.3% Slow AM 62.7% 30.7% 4.2% 2.5% IP Car IP 0.0% 45.6% 44.6% 9.9% PT IP 0.0% 35.1% 46.4% 18.5% (PM is slightly larger, uses IP for consistency) Slow IP 0.0% 69.5% 28.2% 2.3% PM Car OP 0.0% 0.0% 36.3% 63.7% PT OP 0.0% 0.0% 13.0% 87.0% Slow PM 0.0% 0.0% 56.5% 43.5% Bold indicates the largest number in the row CSRM Model Development Report 27

36 :- =586675&; CSRM does not fully model the weekday Off-Peak period (00:00-07:00 and 19:00-24:00). It is never assigned, nor is there sub-mode split or congestion modelling for it. The Off-Peak exists in the model as a choice ( sink ) for Home-Based Work and Home-Based Employer s Business time of day, so that these trips can choose to travel before or after the peak periods. Many of these trips are likely to be in the shoulder periods of 06:00-07:00 and 19:00-20:00, but no explicit assumptions have been made about their distribution within the off-peak period It is therefore necessary to have cost skims for those two purposes for all four main modes in the off-peak period. This is done as follows: Off-Peak car takes distance and time skims from a SATURN LHM run where a matrix of very small values has been assigned. This estimates the network times without congestion. These values are fixed and do not respond to changes in the number of Off-Peak trips. Off-Peak Public Transport takes distances and costs from the Inter-Peak model, and times from the Inter-Peak but with 15 minutes added to represent additional waiting times. walk and cycle level of service does not vary by time period in the model so the Interpeak skims are also used for the Off-Peak The demand model for Home-Based Recreation and Visiting uses Off-Peak cost skims when calculating the costs for tours starting in the PM peak, as in Table CSRM Model Development Report

37 3 Network and Assignment ": 855< As outlined in Section 1.4 the Cambridge Sub Regional Model (CSRM) comprises two assignment models a SATURN model used for detailed highway assignment plus a multi-modal network submodel providing inputs for all other modes to the transport demand model. This chapter of the report describes the network submodel for public transport, walk and cycle An integrated multi-modal transport network has been defined comprising a set of links and nodes for different modes of travel. The main components of the network are a representation of the public transport (PT), walk, cycle and intra-zonal links. A series of access links are coded to connect each zone within the model to the appropriate modal network The road network used in the PT-Walk-Cycle assignment submodel has been extracted from the SATURN local highway model (LHM) (Atkins, 2008). Here, the extracted highway network is used for the assignment of bus services, as well as for the assignment of walk and cycle trips. Public transport services are coded on transit lines, which for bus make use of the physical highway network, and for rail and Guided Bus the network of physical rail track or bus guideways represented within the PT-Walk- Cycle assignment submodel The intra-zonal network consists of non-physical links that connect the zone centroid with itself, which are used for the assignment of intra-zonal walk and cycle movements. Intra-zonals are also defined for the car mode, although they are not assigned in the LHM. The model assumes public transport is not used for intra-zonal movements, as the TDM zones are typically too small for this to be a sensible option Output congested highway link times from the LHM are transferred to the network submodel and affect the route choice of bus and car Park and Ride journeys in the PT-Walk-Cycle network submodel Figure 3-1 shows the base year TDM multi modal network with an indication of highways, physical rail track, cycle/walk, and bus only lanes This remainder of this chapter reviews the design of multi-modal networks, the approach to path building and assignment, and the cost functions used to provide the supply characteristics for the demand model. This includes the intra-zonal network and the network outside the study area This chapter is primarily about route choice and assignment of Park and Ride, public transport, walk and cycle trips that are implemented within the PT-Walk-Cycle assignment submodel. See the LHM Validation report (Atkins 2008) for more information on the highways network development and SATURN light and heavy vehicle assignment CSRM Model Development Report 29

38 Figure 3-1: TDM 2006 multi-modal network The TDM network submodel provides the cost, time and distance elements of the disutility (generalised cost) of travel between each relevant origin and destination zone pair for each mode in each of the time periods modelled. The travel costs are based on public transport fare functions that have been estimated from data assembled for the local area as described in Chapter 4. For the car access legs of park and ride and rail trips with car access the distances and times are taken from the SATURN LHM and the costs calculated using the SATURN ppk values as well as station parking charges. 30 CSRM Model Development Report

39 The path building on the networks is carried out for each of the following main modes: Park and Ride, bus, Guided Bus, rail, cycle and walk The modes walk and cycle have their supply characteristics determined by network paths that minimise the distance which is picked up from the road network links plus extra pedestrian/cycle links. Cycle trips are assigned due to the number of cycle specific facilities such as bridges which currently exist and those which are proposed for Cambridge. Walk trips do not need to be routinely assigned since their routes are unlikely to change significantly Bus supply characteristics are calculated by path building on a service by service basis using the congested SATURN LHM link times in the same fashion as for the bus leg of Park and Ride. Rail path building also uses a service and headway representation of the options, together with feeder connections to stations. The impacts of any rail or bus overcrowding are not currently considered within the model The CSRM multi-modal network consists of: ": Roads for all traffic with light/hgv journey assignment that is carried out in SATURN, Inter-modal connections such as rail and bus stations, Bus / cycle lanes (not for cars), Walk network (pavements and footpaths), Walk/cycle bridges, Rail links (railway track), Guided Busway (analogous to track ), Bus and Park & Ride services, Rail services, and Intrazonal network, roads for travelling within each model zone (for walk, cycle and car journeys only). 3#; 45&##5< 8;85>&0586& A multi-modal transport network comprises a set of links and nodes for different modes or stages of travel. Each type of link is defined to carry particular MEPLAN network modes (journey stages) eg walk to bus / rail, car ride to rail, PT wait etc The network modes / stages of travel defined in the CSRM are shown in Table 3.1 below CSRM Model Development Report 31

40 Table 3.1: Stages of travel defined in CSRM MEPLAN ID network mode Stage AM IP PM Walk Cycle Cycle feeder to Park and Ride and Rail Cycle to PT interconnection (cycle park) Walk dummy (internal creation within PT-Walk-Cycle network submodel) for entry/exit PT links Bus ride Bus access/egress for PT service connectivity Car drive to bus Park and Ride interchange Rail ride Rail access/egress for PT service connectivity Car drive to rail interchagne Guided Bus ride Guided Bus access/egress for PT service connectivity Car parks at Cambridge rail services Car parks at Huntingdon rail services Car parks at Ely rail services Car parks at St Neots rail services Car parks at Local rail services Car parks at Cowley/Milton (in future year networks) bus Park and Ride services Car parks at New Market bus Park and Ride services Car parks at Babraham bus Park and Ride services Car parks at Trumpington bus Park and Ride services Car parks at Madingley bus Park and Ride services Car parks at St.Ives Guided Bus Park and Ride services (in future year networks) Car parks at Longstanton Guided Bus Park and Ride services (in future year networks) CSRM Model Development Report

41 3.2.3 The link types are defined to differentiate types of road, different access stages for public transport (access, wait, board, alight, interchange etc), main mode stage (car, bus ride, walking etc). The full set of link types which are defined in the PT-Walk-Cycle network submodel for the AM peak are shown in Appendix C with a summary shown in Table 3.2 below. Table 3.2: Outline of link types within multi-modal networks Link type ID Description AM peak period 1 to 5 Types of road 11 to 16 Different walk / cycle access stages 21 Car drive to Park and Ride site 30 to 39 Different bus access / interchange stages 40 to 49 Different rail access / interchange stages 50 to 51 Walk network 60 to 65 Car parking at different rail stations 70 to 77 Car parking at different Park and Ride sites 80 to 89 Different Guided Bus access / interchange stages 95, 96 Links for intrazonal travel Interpeak period As for AM peak, eg. Type 201 is the same as type 1 AM peak PM peak period As for AM peak, eg. Type 401 is the same as type 1 AM peak The interpeak link types are defined in the same way as the AM peak types with 200 added to the link type, and PM peak with 400 added. No network is defined for any other time periods The network is duplicated for each time period using the same node definitions. A similar definition of modes ensures that demand for travel in a time period occurs on the network for that time period. Thus the modes allowed to use link type 201 are the same as those on link type 1 with 100 added to the mode number Nodes have a XXX.YY form of numbering where XXX is conventionally used to denote the zone number in which the node is located. The PT transit and interchange nodes are automatically generated when producing the bus, Guided Bus and rail transit lines. During that process a number of new wait, board/alight, access and egress links and nodes are created The main attributes defined on the network are the types of links (roads, railways), lengths, speeds or times and capacities. The following sections provide details on the connectivity of network and characteristics defined for each of the main modes of travel CSRM Model Development Report 33

42 ":" 19#5< 8;&# Bus services are coded as MEPLAN transit lines, which make use of the physical highway network which has been defined from the SATURN highway network plus bus-only roads/bus lanes, which have their own link type and need not be part of the LHM. The highway nodes are used as bus stops where appropriate. Information on routes, frequency of services and total journey time has been taken from timetables (CCC, 2006) Figure 3-2 shows the internal bus network for the 2006 base year, including all bus transit lines (public transport services). Figure 3-2 Base year bus services including Park & Ride buses Bus trips access the network via a two-way access/egress, or a combination of walk links towards a highway bus access node (walking to a bus stop) and then board onto the bus transit line as shown in Figure 3-3. This board time includes a wait time dependent on the headway (times between vehicles) of the transit line being boarded, plus a standard time of four minutes for boarding/buying a ticket. The board link connects the access bus node to a transit node where passengers can interchange between services. Passengers then ride along the bus transit line and then connect back to a transit node and then highway node at the end of that part of the journey via an alight link. A standard time of four minutes is coded for these board links and CSRM Model Development Report

43 seconds for alight links. The passenger may then either connect to a new bus transit line (via another board link) or egress via the access/egress link to the payment network and walk to destination zone / centroid. Transit node 5 Bus transit 30 Transit node 5 Bus board 33 Bus alight 34 Interchange to other bus service Bus alight 34 Interchange node 7 Interchange node 7 Bus board 33 Centroid Bus Access Walk depart 32 Pedestrian node Physical highway Bus egress 35 Walk to destination Centroid , 37 Walk arrive Walk Walk Possible walk access or interchange from another bus service Pedestrian node 3 Walk along pavement Pedestrian node 3 Walk across network or to another PT service or Centroid Red: Blue: Link type Node type Figure 3-3: Link connectivity for bus journeys Even though transit lines are a distinct link type to themselves, buses are still affected by congestion along highway links. The time taken to traverse each link is the maximum of the time coded for the service and the congested highway link time. Transit lines are effectively defined paths through the highway network along which buses travel In general bus trip involves only bus ride and walk access mode. Buses are reached from the pedestrian network via a Bus Access link accessing the Bus Interchange node, which may be thought of as the bus stop At Park and Ride sites cyclists are also allowed to access the Bus mode. The linkages that operate at these sites for cycle access are the same as those which operate for cycle access to Rail stations as depicted in Figure 3-4 below. Since cyclists cannot access the Bus mode at any other stop, these linkages have not been included in Figure Bus trips are allowed to interconnect with other bus services only. This interchange may occur at the interchange node, accessed by the Bus Alight link. From the interchange link, Bus Board links exist for each service that may be accessed at that physical node. To alight from the network, users must egress to the pedestrian node On reaching their destination and alighting to the access node, passengers may walk to the centroid or walk along the network to either access an alternative centroid or to pick up another bus service elsewhere The complete list of bus services that are represented in the base year model can be seen in Appendix D CSRM Model Development Report 35

44 ":( &3#5< 8;&# Rail mode sits at the top of the public transport hierarchy and can therefore make use of, or be accessed by any other mode. This works in a specific way: 1) Walk, cycle or drive to the station, 2) Use the rail service, 3) Interchange to another rail service or alight rail entirely, 4) Either access the centroid to end the journey or continue the journey on bus, Guided Bus or walking, with further interchanges allowed until the journey is finished Access to rail can be done via walking, cycling, by bus / Guided Bus or driving to the station. For travel from external zones, access is achieved directly from the centroid via the direct non-car Rail access link. Town-dwellers may walk across the network and access the rail station via the Rail access link that leads from the access node nearest to the station Drivers that access rail stations by car traverse a parking link in order to reach the rail node. Cars are blocked from using any other PT service before reaching the rail station, since they will be prohibited from using the Bus/GB Entry links The rail transit system is accessed via Rail access to the interchange node and then services may be accessed via Rail Board In a similar way to bus, once a rail journey has been completed, it is possible to interchange onto another rail service directly by alighting from the service via Rail Alight to the interchange node and then picking up a Rail Board link for a connected service Once the rail part of the journey is complete, the traveller reaches the access node via Rail Egress from Transit. From here, the centroid may be accessed directly (usually in external locations) via Rail access (which is 2-way), or a bus or Guided Bus service may be accessed. This is done by accessing the bus interchange node at the rail station via Access Bus/ Access GB. This is made possible since we do not prohibit rail from getting access to buses via the PT node, but do from the physical network The walk network may also be accessed, allowing users to travel to nearby access nodes. From here, alternative centroids or any other PT mode may be accessed to continue the journey This complex series of linkages can be seen diagrammatically in Figure CSRM Model Development Report

45 Rail Transit node 6 Rail transit 40 Rail Transit node 6 Rail board 43 Rail alight 44 Interchange to other rail service Rail alight 44 8 Rail Interchange node 8 Rail board 43 Transit node 6 Rail egress Rail access from transit Pedestrian to transit Walk, or Access node Pedestrian from other PT Pedestrian Rail egress* 3 Rail access* node (Rail St. to 3 (bus or GB) Road) (Road to Rail St.) Physical rail track (between physical rail nodes) node /50, 35/ / Centroid 1 11 Cycle access Red: Blue: Drive to Station (assigned in LHM) , 37, 17 Cycle ride Link type Node type 7 Board bus or GB service 33/83 Bus/GB Transit node 5 Bus/GB Interchange node Walk across network or to another PT or centroid Car Park at Station Car Unpark at Station or or 16 Cycle Park Access bus or GB service 32/82 16 Cycle Unpark 2 Highway node Continue journey on Bus or Guided Bus Cycle ride 2-5, 37, Highway node 21 Cycle egress 12 1 Centroid Drive to Destination (Assigned in LHM) 1 Centroid Note: * direct access/egress from a zone centroid to rail station access node (link type 41) occurs only in external areas or internal rural areas. Figure 3-4: Link connectivity for rail journeys with and with no car access In all cases the travel demand model works with door-to-door matrices for rail. The paths for the door-to-door journey accounts for the individual travel stages for rail, such as accessing to station, waiting, boarding, alighting, as well as on-board travel in train or tram Train services of different stopping patterns within a time period are coded separately, representing the frequency / headways of the services. Rail over-crowding is not included in the current version of the model but could be added at a later stage The rail physical infrastructure is represented in the Figure 3-5 below the complete list of rail services included in the base year can be found in Appendix D CSRM Model Development Report 37

46 Note: Direct access/egress from a zone centroid to rail access node (link type 41) occurs only in external areas or internal rural areas. All zones have a car access to the internal rail network. Figure 3-5: Rail Network The rail submode models the ability to drive to various stations and park before catching a rail service, where station parking charges are included. The network has been implemented so people can choose between nearest local rail station and one or more of four major stations in the study area: Cambridge, Ely, Huntingdon and St. Neots. Catchment areas were created for all four major railway stations. These define to which of the main stations the zones are connected. Figure 3-6 shows the catchment area for the four major rail stations for car park and ride access. 38 CSRM Model Development Report

47 Figure 3-6 Catchment area of four major rail stations for car access ":* 09519#5< 8;&# Guided Bus works in a similar way to bus, although it makes use of both the guideway and regular highway network. Even if the guideway is not being used, a Guided Bus service remains a different mode to bus, reflecting the different levels of comfort, convenience and image that it is expected to offer CSRM Model Development Report 39

48 3.5.2 Within the Guided Bus mode, interchange to conventional bus services is allowed, since Guided Bus is above Bus in the public transport hierarchy. This takes place at the interchange node. This is allowable as GB mode can use both GB and Bus Board links. The PT node is accessed to alight the network or to use the walk network to a different location. An interchange node can be produced for multiple services at the same point, so access to other Guided Bus routes may be gained directly from the interchange node. Like Bus the board and alight links associated with Guided Bus travel have times coded on them. Unlike Bus the time coded on a board link is one minute and 20 seconds is coded on the alight links. Transit node 5 GB transit 80 Transit node 5 Centroid 1 GB alight 84 Direct access Red: Blue: GB board 83 GB access 15, 81* 15\50, 35 Walk OR access from other bus service 3 Link type Node type Interchange node 7 82 Pedestrian node Physical Guideway 87 GB alight 84 Interchange node 7 GB egress 85 Pedestrian node Access other bus service Board bus Interchange to other GB service GB board arrive 51, 81* Interchange node 7 Walk across network or to another GB/bus service or centroid Centroid 1 5 Continue journey on Bus Transit node Note: (81*) Direct access/egress from a zone centroid to guided bus access node (link type 81) occurs in internal rural areas outside Cambridge city. Figure 3-7: Link connectivity for Guided Bus journeys with no car access It is possible for cyclists to access the Guided Bus mode at the Park and Ride sites. The linkages that operate at these sites for cycle access are the same as those which operate for cycle access to Rail stations as depicted in Figure 3-4 above. Since cyclists cannot access the Guided Bus mode at any other stop, these linkages have not been included in Figure Where car is used to access the Guided Bus network it is considered to be part of the Park and Ride mode. No Guided Bus services are included in the Base year model since the Guided Bus was not in operation in ":+ &; &#519&#09519#5< 8;&# A Park and Ride trip uses a journey comprising a car drive and bus (or GB) component, but not rail. The car drive component occurs at the beginning or end of the the journey not both. In order to access bus, the user must traverse an interchange parking link. This connects from the highway to the bus access node and hence bus 40 CSRM Model Development Report

49 may be taken from this point. Any combination of bus or walk can be used from this point onwards in order to reach the destination. Five Park and Ride services are represented in the base model year, with the Guided Bus Park and Ride services being added in the future years. Transit node 5 Bus/GB transit 30/80 Transit node 5 Bus/GB alight (Remaining on same PT mode 34/84 Possible walk access or interchange from another bus service Bus/GB board 33/83 Walk Bus/GB access 15, 50, 81* Park and PnR Site Interchange node 32/82 7 Pedestrian node , Physical highway / Guideway 1-5, 37 / 87 Bus/GB alight 34/84 Interchange to other Bus/GB service (REMAINING ON SAME PT MODE) Interchange node 33/ Bus/GB egress from transit Pedestrian node Access bus or GB service 32/ /85 50 Unpark at PnR Site Zone arrive 51, 81* Transit node Walk across network or to another bus service or centroid Centroid , Highway node 2 Centroid 1 Drive to PnR (assigned in LHM) 21 2 Highway node Interchange node 7 Board bus or GB service 33/83 TRANSFER ONTO OTHER PT MODE Drive to Destination (assigned in LHM) 21 1 Red: Blue: Link type Node type 5 Transit node Continue journey on Bus or Guided Bus Centroid Note: (81*) Direct access/egress from a zone centroid to guided bus access node (link type 81) occurs in internal rural areas outside Cambridge city. Figure 3-8: Link connectivity for Park and Ride journeys The car travel along the network to each Park and Ride site is part of the route choice. This traffic adds to road congestion and is influenced by it within the SATURN LHM. As congestion or parking cost increases, the TDM choice model allows travellers to divert to other distant sites that provide faster and cheaper access to the city centre The form in which bus based Park and Ride in the PT-walk-cycle assignment connects to the car assignment in SATURN LHM merits a more detailed explanation which can be found in Section 6.11 and The zones defining Cambridge city area cannot use Bus Park and ride services to get to the city centre. Figure 3-9 shows the zones, i.e. zone centroids that are linked to the Park and Ride sites CSRM Model Development Report 41

50 Figure 3-9 Cambridge zones without car access to bus Park and Ride sites Ride trips have car links to the appropriate Park and Ride (and station sites), which go directly from the centroid to the base of the parking link. These links do not determine path choice but are coded with the distance and times of the car leg which are extracted from the SATURN LHM to produce the mode and site choice characteristics. ":! <&3;&# Walking and cycling are very important in the Cambridge area and the TDM model explicitly represents these as separate main modes at the top of the choice hierarchy. This structure gives a better representation given the large number of cycle trips in Cambridge which compete as much with short distance car as with walking trips The walking and cycling distances between zones are derived from the walk network and cycle network, which are almost exactly the same. They are both based on the road network, but with additional links both for cycle-only infrastructure and where there are one-way streets, which typically have footpaths in both directions and often contra-flow cycle lanes. 42 CSRM Model Development Report

51 3.7.3 Both pedestrians and cyclists use strict minimum-distance paths in the model, as this provides the best estimate of their minimum-time path (and hence minimum generalised cost, as there is no monetary cost). It is not feasible in a strategic scale model to include detailed modelling of walking or cycling conditions such pedestrian crossings or cycle lanes Distribution and mode split for walk and cycle are also based entirely on distance skims as described in Section 6.3. That is, the model does not assume a speed directly, but only via distance-based parameters. To generate time skims, walkers are assumed to have a speed of travel of 3 miles per hour (4.8 km/h). There is no data available about typical cycling speeds, especially by trip purpose The walk network shadows the highway and cycle only network and is used by the main walk mode as well as walk access modes to public transport. This has several advantages including: Discrete assignment of slow modes on the network, Is removed from the highway network entirely, hence allows any degree of interchanging onto the bus or rail network In future years where pedestrian severance is reduced (e.g. through urban planning and design), or worsened (e.g. in the vicinity of some major new road or rail infrastructure), the walking and cycling distances can be modified to account for such effects. Centroid Walk depart Pedestrian node Walk Pedestrian node Walk arrive Centroid Red: Blue: Link type Node type Figure 3-10 Pedestrian network linkages Cycling can occur on any road link except motorways as well as on any bus only links, as shown on Figure There are also cycle-only links, e.g. the footbridge over the railway in Cambridge. The cycle only tracks (link type 17) were developed in accordance with the Cambridge cycling map available on Cambridgeshire County Council (CCC, 2006). Specific cycle interchange links were added at a limited number of PT termini (e.g. Park and Ride sites, railways stations). These have the same characteristics as car interchange. The difference is that these have zero charge, whereas car parking may warrant a charge depending on the location (usually at rail stations). Centroid Cycle depart Highway node Cycle on Roads/Cycleway/Bus lane Highway node Cycle arrive Centroid 11 2,3,4,5,17, Red: Blue: Link type Node type Figure 3-11 Cycle network linkages CSRM Model Development Report 43

52 3.7.8 For some scenario tests there may not be a need to assign the walk and cycle trips explicitly since their supply characteristics will be invariant across iterations, unlike those of bus and car. Rail passengers are affected by road supply characteristics in principle as they can use cars or buses as feeder modes, albeit this is probably a very small effect. The output modal matrices for walk and/or cycle could subsequently be assigned off-line, to measure the usage of those new facilities that are introduced as part of the complementary measures. ":- #&28#&3&#5&# It is essential for the travel demand-forecasting side of the Transport Demand Model that the distances and generalised costs of intrazonal movements are properly represented. For instance, if car travel becomes more expensive, some people may switch from driving to a supermarket in a nearby zone, to driving or walking to a store in the same zone. Although this is sometimes represented as a frequency-response of the car trip rate, CSRM represents it as a destination or mode and destination shift, and it is important that the intrazonal option is present in the choice model The CSRM model assumes that there are no bus, Guided Bus or rail intrazonal movements. No zone contains two rail stations or Guided Bus stops. For bus, the zones within Cambridge and Huntingdon (which are the main areas of bus use) are generally too small for a bus journey of more than one or two stops. That leaves only walk, cycle and car intrazonals Distance and time are the main intra-zonal transport components, since walk and cycle have no monetary cost and car costs are a linear multiple of distance in the current model. Walk and cycle generalised costs are also calculated directly from distance as described in Section Car time for intrazonals is based on a fixed speed of 20 km/h, plus whatever fixed time and parking costs are normally added to car trips to and from that zone for the trip purpose being undertaken. (That is, the fixed time associated with car is the same for inter- and intra-zonal movements.) Generalised cost is then calculated using values of time in the usual way For realistic representation, and the stability of the demand model, it is desirable that intrazonal distances be no larger than the nearest inter-zonal distance by any mode Therefore, the TDM takes as input an intra-zonal distance for each zone. These were developed via the following steps: A set of draft intrazonal distances were produced by taking half the square root of area of the zone. This is approximately the radius of the zone for a square zone, and so represents an order of magnitude estimate of a typical intrazonal distance. These distances were capped to a maximum distance of 1.5 km, because the nearest village is typically within this range by population and walk trips are unlikely to be longer than that, Distances were further reduced to be no larger than the walk trip to the nearest interzonal zone. The intrazonal distance, time and disutility are almost always less than the smallest inter-zonal distances anyway. This was mostly an issue for large zones on the periphery of urban areas like Huntingdon; in that case, the population tends to be concentrated in a smaller area on the edge of the zone. 44 CSRM Model Development Report

53 If the zone had a single town and no other significant villages, the radius of the town rather than the zone was used as an intrazonal distance, representing the nearby option for most of the population in the zone. Distances that were larger than the car trip to the nearest inter-zonal zone were identified and checked on a case-by-case basis. In a few cases (see below) the SATURN distances were very small due to centroid connector affects, and not comparable. In other cases the intrazonal distance for the zone was reduced. This is often because the zone centroid is actually near the edge, and the population correspondingly concentrated. The hardest case is then the sparsely populated outer area zones or zones with two roughly equal-sized villages. In this case, the intrazonal has typically been left at 1.5 km, as this avoids assuming that most of the population lives close to any attractors in the zone. For development zones on the edge of urban areas that are largely empty in the base year we have estimated an intrazonal distance based on a similar zone or on an assumption of development near the urban edge. (The centroid is typically near the urban edge where development is expected, resulting in very nearby inter-zonal neighbours). Crown copyright. All rights reserved. Figure 3-12: Intrazonal distances by zone (circle diameter is intrazonal distance) ":' 5? 5 #&3 #5 < 8 ; The representation of public transport services from the external to the main study area has been included in a simplified form of straight links that serve as an approximation of non-car rail/bus access to the main study area stations, where appropriate CSRM Model Development Report 45

54 3.9.2 Bus services partially in the CSRM study area and partially in the external area are coded in the same way as internal bus services. The wait times are calculated as a function of the service frequency. It is assumed that far external zones (e.g. Scotland, South-West) do not have bus access to the bus network, i.e. that people would choose either car or train to travel from far external zones to the study area There is no detailed external rail network although the coded rail network stretches to main rail hubs such as London, Peterborough, King s Lynn, Norwich, and Felixstowe (see Figure 3-5). External zones were linked to the most likely point of access on the modelled rail network. For example zones from the north, north-west, and west of the study area would connect through Peterborough on the way to the study area. Speeds on these links is assumed to be 50 km per hour to take into account access time to the rail station, wait time for the train, and the journey time. An average rail fare is coded for those links Each external zone has a possibility of car travel to the study area, or car access to the bus Park and Ride sites of the study area. 46 CSRM Model Development Report

55 4 Input Characteristics (: 855< The demand model responses are made based on the travel characteristics for the various modes and time periods between each zone pair. Many of these characteristics are derived from the network and public transport service representation described in the preceding Chapter 3 or from the SATURN local highway model This Chapter provides information on the remaining travel characteristics that have been input to the model and how these are combined to calculate the disutility of travel on which the traveller choices are based. (: 05#5& &8# The generalised costs of travel throughout the transport model are measured in units of time and include both the money costs and times associated with making journeys Adopting the WebTAG terminology, the disutilities used in the choice modelling are the generalised costs of travel plus additional disutilities such as mode / destination specific constants determined during model calibration The disutility of a complete journey from zone i to zone j is the sum of the disutilities of the individual stages of the journey such as accessing and waiting for public transport in addition to riding on buses, trains and the egress time at the destination. The costs and times of these different elements can be weighted to denote the relative importance when choosing the mode of travel for a journey. In addition, mode specific constants are introduced to represent the inherent attractiveness or the lack of appeal of particular modes to particular journey types The simplified representation of the disutility of a journey by mode for a given purpose, time of day and income is: GenCost mode ij = stages costij β stage + γ stage time stage ij Where: mode stage Dist cost time β γ is the main mode of travel (car, PT, walk or cycle) are the different modal stages of a trip (riding, parking, waiting, walking etc), is the distance (kilometres), is the monetary cost (fuel costs, fares, tolls, parking charges etc) in pence, is the physical time spent walking, riding etc, is the value of time (pence per minute) for the trip segment, is the factor weighting of time to reflect increases in perceived time, eg when waiting for public transport, The cost and time may include terms associated with the destination (or origin), mode and purpose, such as parking charges and times. Disutility is generalised cost plus other constants for example residual disutility terms associated with particular modes, times of day and destinations. Education trips to schools in Cambridge have an additional Cambridge-specific constant in order to reproduce the observed mode split CSRM Model Development Report 47

56 (:" 5=3585&# Because distance and time skims are more typically available from SATURN than monetary cost skims, the vehicle operating costs (VOCs) are implemented in the TDM to reconstruct the monetary cost of car journeys for use in the choice models. The operating cost function and parameters have been implemented to be entirely consistent with those used in the SATURN LHM and were supplied by Atkins (Atkins, 2008) Vehicle operating costs are calculated in units of Pence per Kilometre (format supported by SATURN modelling software, used for highway assignment) and represent the average actual cost to users of running a specific type of vehicle for a number of kilometres VOCs can vary by purpose, income and time of day, though at present there is no variation by income. The Department s guidance on vehicle operating costs indicates that travellers on business perceive more vehicle operating costs than just the fuel costs. The elements making up non-fuel component of vehicle operating costs include oil, tyres, maintenance, and depreciation, and vehicle capital saving. These elements are included in total VOC for vehicles used in working time (business journeys) Fuel consumption is estimated using a function of the form (DfT, Feb 2007, Values of Time and Operating Costs, TAG 3.5.6): Where: L = a + b.v + c.v 2 + d.v 3 L = consumption, expressed in litres per kilometre; v = average speed in kilometres per hour; and a, b, c, d are parameters defined for each vehicle category The parameters needed to calculate the fuel consumption element of VOCs are given in TAG (DfT, February, 2007). Atkins took this information and used the TAG parameters with average speeds of travel by time period to obtain an average pence per kilometre cost which is used in the model (Atkins, 2008) The VOC is used in providing a monetary value to the distance received from the SATURN highway model as described in Section The following table summarises the vehicle operating costs implemented for each of the time periods. The variations seen are due to the variation in speeds by time of day. Table 4.1: TDM base year (2006) vehicle operating costs (VOC) Purpose/ Value of time category 2006 Pence per Kilometre per vehicle AM IP PM Employer s Business / Work car All other trips / Non work car (:( 5=3589&# Light vehicle (car) occupancies were also supplied by Atkins and are based on Department for Transport TAG (see LHM report, Atkins, 2008) WebTAG provides the car occupancies for Commuting, Business, Other and Average car trips with no further segmentation by income level. The figures in WebTAG were based on the National Travel Survey. 48 CSRM Model Development Report

57 4.4.3 The car occupancy values, as calculated by Atkins, are given for the following groups of trips: HBW, Other (shopping, personal business, visiting friends and relatives), and Employer s Business. The table below shows vehicle occupancies for the base model year The occupancies for Education trips vary by direction for the AM Peak and in the Interpeak because the model includes Escort trips within the set of Education trips, such as a parent taking children to/from school, and their return legs without children in the Home-Based tour. At these times there are likely to be more occupants within each car in one direction than in the other because of this. In the PM peak it has been assumed that most of the travel is Tertiary, and the model adopts the same vehicle occupancies as for Other trips. Table 4.2: Vehicle Occupancies for the Year 2006 Purpose Vehicle occupancy (2006) AM IP PM From Home To Home From Home To Home From Home To Home Home based work Employer s Business (HB and NHB) Education Other (HB and NHB discretionary) (:* & Values of time (VoTs) are used to represent the relative importance of cost versus time for different trip purpose and traveller type segments, particularly when converting time and cost into a generalised cost measure. The DfT s TAG unit provides guidance on the use of values of time within a transport model. This suggests that three basic values of time should be used for: Business travel (travel in work time) Journeys to work (commuting) Other personal travel (recreation etc) TAG unit 3.5.6, and more specifically unit for road pricing, also recommends further variation by income. The detailed guidance in Annex A of TAG unit provides a detailed specification of how values of time can be derived from incomes. The WebTAG guidance includes a recommendation for using 3 groups of travellers based on: Incomes < 17,500 pa 17,500 < income < 35,000 pa Income > 35, The DfT in their feedback during the model development confirmed that the three income bands above are in fact in 1997 prices CSRM Model Development Report 49

58 4.5.4 Prior to this, the TDM model had already been implemented to work in 2006 prices consistent with the RSI data which collected data on these income bands in 2006 and hence in 2006 prices. It was agreed that the TDM would continue to work with the above income bands in 2006 prices to be consistent with the data available The values of time from the TAG income bands were adjusted to those being used in the model through a two stage process: Adjustment of the TAG bands and values of time to 2006 values and prices, Estimation of values of time for the proposed bands in 2006 values and prices The work done started from the ITS/Bates value of time study (Report to DfT, 2003) and moves the values through time. The results from this process for 2002 confirmed that the TAG income bands are defined in 1997 values but that the values of time for the bands have been up-rated to 2002 prices. This process gives the values of time per person trip shown in Table 4.3. Table 4.3: Values of time for CSRM in 2006 prices and values Income band (2006 prices) Commute ( /hr) Other ( /hr) EB driver ( /hr) EB pass ( /hr) Low (< 17,500) Medium ( 17,500 to 35,000) High (> 35,000) (:+ &; #0=& The information used in order to derive parking charges for the model is from the various District Council websites A summary of the overall method is: From the list of the main car parks and charges in Cambridge city area, a typical short stay and long stay charge were selected. Parking charges for other non-cambridge areas assumed based on local knowledge. Each transport zone has been assigned a representative charge based on proximity to a car park. Each transport demand flow has been assigned a representative charge type (long, short or none). 50 CSRM Model Development Report

59 Table 4.4 Car park charges in Cambridge (2007 pence) NAME CSRM Spaces PRICE (PENCE) BY NO. HOURS DEMANDED Zone 1 hr 2 hr 3 hr 4 hr 5 hr >5 >6 group hr hr Castle Hill Car Park Outer Park Street MS Inner (a) Grafton West Car Park Inner (a) Grafton East MS Inner (a) Queen Anne Terrace MS Inner (b) Lion Yard MS Inner (a) Gwydir Street Car Park * Outer n/a n/a N/a n/a n/a Adam and Eve Inner (a) n/a n/a N/a n/a n/a Source: CCC website, For each car park a cost was identified for a typical short and long stay. The true averages depend on the actual average length of stay, the number of people using parking season tickets and the car occupancy. An estimate has been used where the short stay cost is equivalent to a two hour charge and the long stay average estimated cost is equivalent to a five hour charge as highlighted in bold in Table Groups of Cambridge zones were then associated with charge levels as parking in the historic centre is more expensive than parking on the edge of the centre For non-cambridge areas, the local data has been analysed, and the following assumptions on short and long parking charges assumed for the urban areas. No charges are applied in other villages / rural areas. Table 4.5: Assumed parking charges in urban areas outside Cambridge (2007 pence) Area Short stay Long Stay Huntingdon, St. Neots, St. Ives Ely For all parking charges the CHAW RPI index was used to scale back prices from 2007 values to 2006 values see ONS (2007). The resulting TDM charge level assumptions are shown in below table. Table 4.6: Parking charges by geographical area (2006 pence) Zone Group SHORT STAY (2 hours) LONG STAY (5 hours) Cambridge Inner-Central Cambridge Outer-Central Other urban areas The composition of each zone group detailed in Table 4.6 is shown geographically in Figure 4-1: CSRM Model Development Report 51

60 Figure 4-1: Zones / areas with parking charges The assumed length of stay varies for different trip types as follows: Commuters pay the long stay charge. All other flows pay the short stay charge except: Education trips incur no parking charge (education trips are generally dropping off or picking up children from school and will not involve paying for parking). However, there are Cambridge-specific mode specific constants for the various Education age groups which partially reflect the difficulties and perceptions of driving in Cambridge. For tertiary students the car constant may include some of the effect of parking restrictions, although data about tertiary mode choice is very limited. 52 CSRM Model Development Report

61 4.6.9 The current parking structure may overestimate charges paid by commuters, as many travellers have access to private non-residential parking, and different trip purposes have different proportions paying public rates for parking. For other purposes such as business, shopping, and recreation a significant proportion of travellers will pay for parking, though for shorter time periods. The model currently assumes that all commuters are paying for the parking if their journey ends (workplace) within charged zones. If more data became available and there was a requirement to model parking in more detail the assumptions can be revised The inconvenience time associated with finding a parking space in Cambridge city centre is assumed to be 10 minutes for commuting journeys and 15 minutes for education and other For West Cambridge we have assumed that this time is 5 minutes for education journeys and 2.5 minutes for other journeys For all other internal zones we have assumed that the time to find a space is 2.5 minutes for all purposes, and for external zones we have assumed that this time is 5 minutes for all purposes Parking at stations and at Park and Ride sites we have treated differently, and parking times for these places are independent of the zone in which the site is to be found We have assumed that the search time at a Park and Ride site is 3 minutes for all purposes Searching for a parking place at local stations we have assumed to take 15 minutes as there is a shortage of places at these stations. We have assumed that parking at one of the major stations in the Study Area (Cambridge, Huntingdon, Ely, St Neot s) will require less time. For Cambridge a 12 minute search is assumed, and for the other three we assume finding a parking place requires a 7 minute search These times are constant for all times of day. Table 4.7: Car park charges at rail stations STATION SPACES DAILY CHARGE before 10AM (pence) DAILY CHARGE off peak (pence) Cambridge Huntingdon Ely St. Neots Source: National Rail website (2007) Parking charges have been implemented in the model for the four major stations within the Study Area, as shown in Table 4.7 above The charges have been implemented on the links associated with parking/unparking at the stations concerned. Station choice is carried out at the trip level rather than at the tour level, so to better balance station choice we apply half the charge for both parking and unparking For local rail stations there is no charge for parking (or unparking) but there is a time penalty for arrivals in the InterPeak to represent the shortage of places at these stations CSRM Model Development Report 53

62 (:! 913&#86&5 Rail fares The data sources used in order to derive the CSRM rail fare structure are: Information on season tickets: Cambridge railway station, Other fares: thetrainline.com, The use of different ticket types by trip purpose: Department for Transport, National Rail Passenger Survey, Office of Rail Regulation, Rail fares are categorized into commuting fares, education, business and leisure fares for each of the TDM travelling purpose Fares data was gathered on thirty different trips (zone pairs) across a range of distances. For each trip the fares collected were Weekly, Monthly, Annual season ticket price, Regulated return (typically cheap day return ), Unregulated return (typically standard day return ), and Standard first class return fare. A single journey charge (SJC) was then calculated for each type of season ticket Different ticket types and their usage by journey purpose have been assigned to different journey purposes according to the information in the National Rail Passenger Survey Four trend lines were fitted to the obtained single journey charge data points, with straight lines providing the best fit for business fares. For commuting and leisure fares, a distance decay function was found to be more appropriate. It was found necessary to adjust the fare paid for trips to/from London, as these are often more expensive than other journeys The form of the fares function is: Fare = Constant + DistPar Distance 1 ExpPar Where Constant is a fixed cost per trip DistPar Distance ExpPar is the cost per kilometre travelled is the distance travelled by rail is the parameter to convert the function from linear to a distance decay function (Exp=0 when linear) The table below shows the estimated fares function for single journeys not ending/starting in London. Fares with a trip end/start in London were adjusted by adding the following additional cost to the constant term: a) Business: +450 pence b) Leisure: +350 pence c) Commuting: +200 pence There is also a further discount on the top of the calculated fare so that certain sub-groups (children, OAPs, full-time students) will pay only a proportion of the value produced by these functions. 54 CSRM Model Development Report

63 Table 4.8: Estimated single journey (one-way) rail fares non-london (2007) Business Leisure Commuting Function Type Constant Power Power Constant (Pence) Pence Per Km ExpPar N/A (=0) London additional constant (pence) Special season tickets including a fifty per cent discount are available to school children (inquiry at Cambridge rail station). Therefore for education trips the assumed fare is half of the commuting function noted above A proportion of young persons (under 26) and retired people are also likely to use railcards. However, the main obstacle of representing this in the model is that the number of eligible people who choose to purchase railcards is not currently known. For this version of the model it has been decided to ignore railcards on leisure tickets until further information is available The CHAW RPI index was used to scale back 2007 nominal prices to the model s real-terms currency units of 2006 pence. The real terms rail fare changes were then applied to 2007 rail fares Fares are coded for each trip modelled thus the single journey cost is coded for each leg of the journey and the complete tour will pay twice this charge. Bus/Guided Bus fares In order to determine bus fares: a) Data on the adult return fare was collected for a number of bus services both inside and outside of Cambridge in 2007 (Stagecoach, 2007). b) The data was graphed with the curves fitted through them in order to obtain the price of an adult return fare as a function of distance. c) Data on Travelcards and other zonal fare mechanisms as well as other discounts for sub-populations were collected. This would include for example, payment levels for weekly travel cards and information on concessionary fares. d) The realistic payment level was found relative to half the adult return fare. For example, schoolchildren and OAPs would pay 50%. Regular bus commuters have a propensity to use a zonal travelcard product Functions were fitted to the observed data on fares in Cambridge and outside Cambridge. This represents the estimated Single Journey Charge (SJC). To account for the pre-dominance of the day rider flat fare ( 2.80 return in 2007) the Cambridge function is constant for most zone pairs in the Stagecoach Cambridge area. A power function was found to work best for the external area CSRM Model Development Report 55

64 Cambridge Non_Cambridge Sgl Journey Cost (pence) Distance (km) Figure 4-2: 2007 Bus fares by distance (2007 prices) The Stagecoach Cambridge area zones with a constant fare function are shown in Figure 4-3. Figure 4-3: Day Rider Zones (2006 Estimate) To calculate an appropriate average bus fare, the use of travel cards and concessions was considered. It has been assumed that primary school students travel half price, that other students receive a third off the single journey charge and that people over 60 years of age travel at half price. Free travel for pensioners was introduced after The proportions of those travelling for each purpose for the AM Peak and InterPeak who bought each particular class of ticket available have been estimated 56 CSRM Model Development Report

65 using the data available. The classes constitute Full Price Fare, Daily and Weekly Travelcards, Discount Tickets for Students and Over 60 Fare Tickets. By assigning to each class of ticket the appropriate discount we can use these proportions to calculate the proportion of total ticket price that the average traveller by each purpose within each TOD paid, and this data is shown in Table 4.9. Table 4.9: Estimated Fare Fraction Paid by Traveller Category Travelling category % paid AM peak % paid Inter peak COMMUTING 72% 76% EDUCATION 64% 61% OTHER 73% 73% BUSINESS 94% 94% The CHAW RPI index was used to scale back 2007 nominal prices to 2006 currency. The real terms bus fare changes were then applied to 2007 bus fares Guided Bus is not implemented in the base year but will be introduced in 2011, and will be covered in the CSRM Forecasting Report. In the present model, the fares for Guided bus have been assumed to be the same as for regular bus services. (:- 913&#8&5&#<& For walk access to and egress from PT services a fixed speed of 4.8 km/h is assumed. Cycle access, where possible, assumes 10 km/h plus a cycle parking penalty term of several minutes to encourage short access journeys to use walk access. The walk and cycle main modes use different parameters based on distance to calculate the generalised cost, see Section 6.3. Both pedestrians and cyclists use strict minimumdistance paths in the model to access PT services Rail access from external or rural zones with no detailed highway network were linked to the most likely point of access on the modelled rail network by rail access links. A speed of 50 km per hour is assumed on these links to take into account access time to the rail station, wait time for the train, and the journey time. Rail access/egress connectors in external or rural areas are represented as a set of straight lines that represent the crow-fly distance between the centroid and the target rail station The times (speeds) of car legs (car access) in Park and Ride and rail movements are skimmed from the LHM s congested SATURN assignment The PT wait times are dependent on the headway of the bus, Guided Bus or rail service being accessed. These wait times or wait penalties are added onto the boarding links of a PT journey (link type 33 for bus boarding, 43 for rail boarding, and 83 for Guided Bus boarding; shown previously in network connectivity diagrams: Figure 3-3, Figure 3-4, Figure 3-7, Figure 3-8) The waiting time for a PT service is a function of the headway. This function is the same for bus, Guided Bus and rail modes in all time periods, although the headway itself may vary between periods. For trips of up to 10 minutes it is assumed that the traveller has a random wait, with average time being half the headway. As the headway grows longer, the average time increases but more slowly because at least some of the travellers will use timetable information to arrive closer to the expected time CSRM Model Development Report 57

66 4.8.6 The function is headway if headway 10 min wait time = headway-10 headway > 10 min This wait function is plotted in Figure 4-4. At present, an hourly service will induce approximately a ten-minute wait and a four-hourly service will induce an eighteen minute wait. PT Wait times (minutes) Headway Figure 4-4: Relationship between PT wait time and service frequency In addition to wait times, an extra minute of boarding time is added to Guided Bus board links during the model assignment, and four extra minutes of boarding time is added to Bus board links The time to alight Guided Bus services is assumed to be 20 seconds. We have assumed that it takes 30 seconds to alight from a Bus service For rail travel this time is 1 minute, plus an extra minute for the traveller to exit rail station. (:' < 50=# Weightings of time are used to distinguish the time spent accessing and waiting for public transport from the other journey stages when constructing the generalised cost of travel on which the travellers choices are based. They also represent the general reluctance of people to walk long distances or wait for a public transport due to the perceived time required that may not be the same as actual time The weights used within the model are shown in Table 4.10 below. (Network modes are defined in Section 3.2). 58 CSRM Model Development Report

67 Table 4.10: Weighting of time components in generalised cost Stage of travel Network Mode Path assignment Mode choice In Vehicle Bus Ride 1 1 GB Ride Rail Ride Wait Time Bus wait GB wait 2.5*0.9= Rail wait 2.5*0.8=2 2 Access Walk access 3 2 Cycle Access 3 2 Cycle Park Note: TAG Unit (February 2007) recommends that the values for nonworking time ( commuting and other ) spent waiting for public transport is two and a half times the commuting and other ride time values. Where walking and cycling is used as a means of inter-changing between modes of transport, the non-working values ( commuting and other ) of walking and cycling is twice the standard commuting and other values. The current version of CSRM is in line with the TAG assumptions Walk and cycle generalised costs are based on distance as described in Section 6.3, so any weighted time for access is added based on assumed fixed walk or cycle speed. This is equivalent to a time weighting of 3 or 2 as shown above for the different choice stages. A higher value of 3 is used in path assignment to ensure the walk stages on PT trips are not excessively long or frequent The rail time weighting of 0.8 reflects the greater comfort and ability to use time productively on rail services The Guided Bus time weighting is a forecasting assumption as there are no GB services in operation at the time of writing. As the GB services are intended be of high quality and comfort (eg. leather seats and wireless Internet access), and the ride quality on guided sections should be superior to normal bus services, the GB VoT weighting has been chosen to be between that for bus and rail There is some literature support for this, see for instance Whelan et al. (2008) who conducted a Stated Preference experiment in the north of England into Bus Rapid Transit (BRT) which found that the public s percieved time weighting for BRT was 92% of the time weighting for conventional bus. Light Rail was discovered to have a perceived time weighting 78% of the same CSRM Model Development Report 59

68 5 Travel Demand Input *: 855< As outlined in Chapter 1 of this report, the transport demand model is linked to a land use model for the sub-region. The land use model forecasts the disposition of both residential and employment activity. The population and employment figures output by the land use model are used directly as inputs to the travel demand model. The number of trips (trip productions) taking place are calculated by applying trip rates to the population from the land use model. The numbers of trip attractions, or weights for singly constrained trip distribution models, are derived from the employment and other land use model outputs This chapter of the report explains which planning data figures are taken from the land use model and how they are used within the travel demand model. The derivation of the trip rates used is also set out below. *: 893&8##96805#5&8# For each travel demand model zone, the numbers of trips productions are derived by applying a trip rate to the numbers of people that are generated within the land use model Within the land use model the population resident in households is categorised by socio-economic group, household size (one or multi adult) and household car availability categories The land use model is implemented initially in 2001 to make extensive use of the 2001 Census information available. A forecast is then carried out for 2006 to provide the planning data inputs to the base year of the CSRM travel demand model. For this 2006 run the inputs to the land use model ensure that: Residential development (dwellings) by zone are in line with the changes in stock based on figures provided by Cambridgeshire County Council. The numbers of households generated by car availability level for the sub region are in line with the TEMPRO 5.4 forecasts for The employment levels in the land use model by industry sector and work status (full or part-time work) match the TEMPRO 5.4 figures for the sub region for Population data is extracted from the land use model for three main groups of the population as follows: Employed adults Children and students Non-employed adults (ie the rest of the population) The dimensions available from the land use model compared with those required for the transport demand model are shown in Table 5.1 below. As can be seen from the table, the dimensions required by the TDM vary by trip purpose. The fundamental difference between the two data sets is the inclusion of INCOME in the TDM. Otherwise, it can be seen that the categories are coarser in the TDM than in the land use model and thus the required population data can be obtained by aggregation of land use model outputs. 60 CSRM Model Development Report

69 Table 5.1: Dimensions of population inputs Trip Purpose Commuting (HBW) Education (HBEd) Employer s business (HBEB) Discretionary trips (HBPB and HBRV) Transport Demand Model (TDM) dimensions (Requirement) - 4 GSeC for employed adults - 2 PT/FT - 3 Car Availability (24 Commuter segments) - 3 * Car Availability - 3 * Income - 4 * Age (education stage) (36 Education segments) - 3 * Car Availability - 3 * Income (9 HBEB segments) 3 * Car Availability 3 * Income (9 HBPS segments and 9 HBRV segments) Land Use Model Dimensions (Data Source) Employed Residents in households - 4 GSeC - 2 PT/FT - 1 Adult HH * 2 Car Availability - 2+ Adult HH* 3 Car Availability Total 40 population segments Employed Communal Establishment Residents (CER) - 1 * PT/FT (assume PT) - 1 * Car availability (PC assumed) - 4 * GSeC Total 4 population segments - 5 * Car Availability - 6 * GSeC - 4 * Age (education stage) Total 120 population segments Students (tertiary) from two sources: A: Communal Establishments - 1 * Age (19+) - 1 * GSeC (GSeC 7) - 1 * Car Availability (NC) B: Wholly Student Households - 1 * Age (19+) - 1 * GSeC (GSeC 7) - 1 * GSeC (GSeC 7) - 3 * Car Availability (NC/PC/FC) Total 4 student segments Employed Residents - 4 GSeC - 2 PT/FT - 1 Adult HH * 2 Car Availability - 2+ Adult HH* 3 Car Availability Total 40 population segments Employed Communal Establishment Residents (CER) - 1 * PT/FT (PT assumed) - 1 * Car availability (PC assumed) - 4 * GSeC Total 4 population segments Employed residents 44 segments as defined for commuting and business above Children / students 124 segments as defined for education above Non employed adults (10 segments): - 2 GSeC (pensioners and non-pensioners) - 1 Adult HH * 2 Car Availability - 2+ Adult HH* 3 Car Availability Total 178 population segments CSRM Model Development Report 61

70 5.2.6 The potential categories into which persons can be classified are: GSeC: General Socio-Economic Category (7 potential categories 4 for employed, 1 for tertiary students, and 2 for the non employed). The employed categories are defined in Table 2.5. PT/FT Job Status: (Part-Time vs Full-Time) Car Availability 3 segments: Full Car (FC), Part Car (PC), No Car (NC) Household Income in 2006 prices: Low (<= 17,500), medium ( 17,500-35,000), high (>= 35,000) Household Size: 1 Adult, 2 or more adults (School) Age: Primary School, Secondary School, Six Form, Tertiary The relationship between the car availability categories in the TDM and those from the land use model is shown in Table 5.2 below. Table 5.2: Car availability definitions in land use model and TDM TDM car availability No car availability Partial car availability Full car availability Land use model car availability 1 adult 0 car 2+ adults 0 car 2+ adults 1 car 1 adult 1+ cars 2+ adults 2+ cars In most cases (except for commuting see para 5.4.9), it is necessary undertake a transformation from the GSeC (socio economic groups) to three income bands in the TDM model at the trip end stage. The commuting trips are converted from GSeC to income at the same stage in the model as they are converted from tours to trips as described in Section 5.4 below. Analysis of data from the National Travel Survey (NTS) provided by DfT for model development work in associated with TIF, showed how the land use model segments related to the income bands required in the TDM. The results of this analysis for the employed residents are shown in Figure 5-1 below As can be seen in Figure 5-1, some categories of employed residents from the land use model (such 2+ adult households with 2+ cars in GSeC 1 and 1 adult households) can be mapped to a single income band with some precision based on the evidence. However other household types are found to be distributed more evenly among the household income bands. The approach adopted was to apply the proportions derived from the NTS data to the disaggregated population data from the land use model and then aggregate the results to the dimensions required for the TDM. 62 CSRM Model Development Report

71 1 Sum of PROB INCOME HIGH MEDIUM LOW NC FC NC PC FC NC FC NC PC FC NC FC NC PC FC NC FC NC PC FC 1 ad 0 car 1 ad 1 + car 2+ ad 2+ ad 2+ ad 1 ad 0 car 1 car 2 + car 0 car 1 ad 1 + car 2+ ad 2+ ad 2+ ad 1 ad 0 car 1 car 2 + car 0 car Figure 5-1: Income profile of employed residents by GSec and car availability The validity of this approach was confirmed by converting the households by socio economic group (GSeC) and car availability to income bands using proportions from the NTS and then calculating the household weighted average income for each zone in the land use model. The zonal income values were found to compare well against the average household incomes available from CACI data (also provided by the Department for the purposes of TIF related model development work). *:" 3&###0&&#968&&8# Home based work (commuting) trips are doubly constrained; hence the TDM requires an input number of commuting trip attractions. The number of commuting trip attractions are determined from the employment (number of employees at the workplace) with trip rates applied to ensure that the number of trip attractions exactly matches the number of trip productions as estimated from the population data inputs (Section 5.2 above). The dimensions of the employment data used for home based work trip attractions are GSeC and full / part time (as defined for the population previously) As for commuting, home based education is also doubly constrained for the school based trips. Those for tertiary education are singly constrained. The school based education trip attractions are calculated using the number of children / students arriving at the school zones from the land use model. In the 2006 run of the land use model these numbers are constrained to match data obtained from the National Pupil Database (NPD), plus supplied data on independent schools 1 (CCC data). The data on school places is segmented by the three school related age groups (education stage) only The other trip purposes are implemented as singly constrained trip distribution models. No trip attraction data is therefore required for input. However to improve the trip distribution a set of attraction weights are input to the model to reflect the relative importance of different zones based on the amount of activity they contain. The data input as attractor weights is also taken from the land use model as summarised in Table 5.3 below. 1 ad 1 + car 2+ ad 2+ ad 2+ ad 1 ad 0 car 1 car 2 + car 0 car GSeC1 GSeC2 GSeC3 GSeC4 1 ad 1 + car 2+ ad 2+ ad 2+ ad 0 car 1 car 2 + car 1 The data on these schools is not contained within the NPD CSRM Model Development Report 63

72 Table 5.3: Source of trip attraction weights Trip purpose Attraction weight Notes HB and NHB Employer s business HB tertiary education HB personal business and shopping HB Recreation / visiting friends & relatives Total employment Tertiary education pupils at place of study Retail floorspace Combination of other floorspace and dwellings No variation for EB trips by income group An output from the LU model An input to the LU model Other floorspace is mainly recreation related eg cinemas, fitness centres, restaurants etc. Both inputs to LU model NHB other Retail floorspace An input to the LU model *:( =8571&505#5&8# The conversion of land use activities into the demand for travel is carried out using trip generation rates derived from the National Travel Survey for a detailed person and trip purpose segmentation The National Travel Survey (NTS) is a continuous household survey on personal travel. It provides the Department for Transport (DfT) with data to answer a variety of policy and transport research questions. The survey has been running on an ad hoc basis since 1965 and continuously since The home based and non-home based trips are generated slightly differently within the model. This section explains how home-based trip rates were calculated The NTS data set used to calculate trip rates for CSRM includes records from inclusive for the whole of Great Britain to meet the requirement of sample size. The trip rate comes from the ratio of the number of trips by segmentations to the associated number of individuals for the same segmentation. The trip rates required for the TDM are the trip production or tour rates ie for home-based trips the number of from home trips made for that trip purpose The time periods for which trips are generated differs by purpose as set out previously in Table Thus for home-based commuting and business trips a 24 hour average weekday trip rate is required; while for home-based education and discretionary trips it is the 12 hour average weekday trip rate which is applicable. The resulting trip rates are shown in Table 5.4 below. The education rates are higher than might be expected since they include the escort education trips as well as those going to school / college. 64 CSRM Model Development Report

73 Table 5.4: HBW and HBEB Trip Generation Rates: 24 hour From-Home trips per employed resident Car Availability Purpose Work Time GSEC Full Car Partial Car No Car Overall HBW Full Time all Part Time all HBW Average HBEB Full Time all Part Time all HBEB Average Table 5.5: Education Trip Generation Rates: 12 hour From-Home trips per person in education AGE Full Car Car Availability Partial Car No car Overall 5 to to to Total Discretionary trips are generated in the Land Use model as person trips per month. However, distribution is carried out in the Transport Demand model, and hence rather than exporting trip matrices, the trip productions (residence factors) and trip attraction weights (commercial floorspace and dwellings) are output separately CSRM Model Development Report 65

74 5.4.7 Within the TDM it is necessary to convert the HB tours into trips from and to home. This step is carried out before the trips are assigned in the network models by which stage most of the tours have been disaggregated by mode and time period. The conversion process therefore requires data for each trip purpose on the number of trips to home by mode and time period for each from home mode and time period. These figures were also derived from NTS data and have been shown previously in Table The first exception to the above is education trips, which do not have any time of day choice, thus 12 hour tour matrices by mode are created. Primary, secondary and 6 th form were combined in the analysis of the national NTS data the results of which were adjusted to generate a better fit for the local RSI cordon data. The proportions currently used in the model by Mode are shown in the table below. Note that a fromhome trip by Car corresponds to to-home trips (school) or 0.85 to-home trips (tertiary) by the same mode. So in Table 5.6, each school tour by Car generates AM from-home trips and AM to-home trips, and so on for IP and PM (The apparent anomaly of one School tour by either Walk or Cycle generating more than one to-home trips is due to a proportion of pupils travelling to school by either Car or PT and then returning by either Walk or Cycle.) Table 5.6: Education travel proportions: 12 hour trips split by time of day and direction HBEd from home HBEd to home Per 12hr from home trip Per 12hr from home trip Mode Time Period School Tertiary School Tertiary 07:00-09: % 15.0% 3.3% 2.0% Car 10:00-15:59 9.2% 52.5% 52.3% 42.0% 16:00-18:59 1.7% 32.5% 20.2% 41.0% Grand Total 100.0% 100.0% 75.7% 85.0% 07:00-09: % 61.6% 0.2% 5.5% PT 10:00-15:59 3.5% 36.6% 43.6% 34.2% 16:00-18:59 0.1% 1.8% 53.6% 41.0% Grand Total 100.0% 100.0% 97.3% 80.8% 07:00-09: % 51.8% 3.8% 28.9% Walk and 10:00-15:59 8.4% 46.3% 69.1% 45.0% Cycle 16:00-18:59 0.2% 1.9% 30.3% 15.8% Grand Total 100.0% 100.0% 103.1% 89.7% The second exception is the HBW trips, which must be converted from the eight GSeC Full/Part Time categories to 3 income segments using income split coefficients which are shown in Table 5.7. Therefore each HBW car or public transport tour generates up to 3 from-home trips (some in each income range), and 3 income tohome trips in that and subsequent time periods, for as many as 9 associated to-home flows. Because walk and cycle do not have income-segmented flows, each HBW walk or cycle trade generates only one from-home flow in one period, and one to-home flow in the same and subsequent period. 66 CSRM Model Development Report

75 Table 5.7: Income split by GSeC for commuters GSeC % high % medium % low 1 65% 24% 11% 2 49% 31% 20% 3 35% 34% 31% 4 21% 34% 44% *:* 05#5&8#86#8#=8571& NHB trips are generated from HB trip attractions (eg. persons arriving at work). All HB trip attractions can generate NHB other trips; only HBEB and HBW generate NHBEB, as the data showed that the number of NHBEB trips following other HB purposes was negligible The NHB trips are generated in each of the three modelled time periods as one-way trips from the HB attraction zone. No NHB trips are generated off-peak The NHB trip rate per HB trip attraction are fixed based on the National Trip End Model (NTEM). The idea is that workers arriving in a given period only generate trips in that and subsequent period. Those arriving in cars can use cars or other modes, with a conditional probability spread, while those not arriving in cars use a probability spread conditional on not arriving in a car, which generally gives much lower car use NHB walk and cycle trips are not generated in external zones, nor can they be attracted to external zones, as the model does not include paths by these modes to such zones. This is unlikely to be a problem as such trips are usually short and the study area boundary does not pass through any urban areas. For car and public transport trips, NHB trips are both generated from and attracted to external zones, which probably over-estimates the inbound trips. *:+ 28#5&=# Trip generation and attraction data from the Land Use mode is modelled on the less detailed LU model zoning system, so zone matching is carried out between land use and transport zones at the interface between the two models. As the TDM zones nest entirely within the land use zones, it is necessary only to split trip ends and attractors between constituent TDM zones The process uses appropriate factors to apportion each trip production and trip attraction as described below Non-home based trips are generated within the TDM itself as above rather than in the LU model and so their productions do not need zone matching. Their size terms (attractors) are zone matched HBEB and NHBEB use the same attractors, similarly NHBO and HB PB/Shop both use the same attractors as in Table CSRM Model Development Report 67

76 Table 5.8: Proportions used for Zone Matching of tours by purpose Purpose Production proportions Distribution model HBW Dwellings doubly constrained HBEB Dwellings singly constrained NHBEB n/a singly constrained HBEd 5-11 NPD pupil 5-11 home ends doubly constrained HBEd NPD pupil 12- doubly 15 home ends constrained HBEd Dwellings doubly constrained HBEd 19+ Dwellings singly constrained HB Dwellings singly PB/Shop constrained HB Dwellings singly Rec/Visiting constrained NHBO n/a singly constrained Attractor (size term) proportions Total commercial floorspace Total commercial floorspace Total commercial floorspace Tertiary floorspace Total commercial floorspace Total commercial floorspace Total commercial floorspace Trip attractions (for doubly constrained) n/a * see below n/a School places 5-11 School places School places n/a n/a n/a n/a n/a Home-Based trip generation (ie. at the home end) is zone matched using dwellings, except for school pupils aged Suppose t is a TDM zone in LU zone L, and M any demand segment such as HBEB high income, full car availability (segmentation is described in Table 2.6 Table 2.9): Let the zone matching factor D[t L] = (dwellings in t in 2006) / (dwellings in L in 2006) Then (trips M from homes in t) = (trips M from L in LU model) * D[t L] Dwelling ratios are also used for 2001, and forecast dwellings are used in each forecast year. Dwellings are used for this ratio in all years because they are a proxy for population and they are available in a consistent way in all years. In forecast years dwellings are an exogenous input to the LU model at a detailed spatial level anyway, whereas CSRM does not normally make population forecasts below the LU zone level Note that trip generation from the LU model is always based on population or population forecasts. Dwellings are only used for splitting them into TDM zones where the two zoning systems are not at the same scale School pupils are zone matched using the home ends of the NPD matrix, as despite the lack of independent school data, these better reflect which parts of each zone contain families with school age children. For 16 year olds and over, the NPD sample was too small for this purpose and dwellings have been used as for other purposes. 68 CSRM Model Development Report

77 5.6.8 Zonal size terms (attractor weights) for all purposes are zone matched in very much the same way as trips. For example, R[t L] = (retail floorspace in t in 2006) / (retail floorspace in L in 2006) Then (HBPB/Shop attractor in t) = (HBPB/Shop attractor in L in LU model) * R[t L] = (total commercial floorspace in L) * R[t L] Again, it should be made clear that the total floorspace is not the basis of the attractors themselves (the HB PB/Shop attractor is largely based on retail floorspace) but is only used to split them, as it happens to be the best available ratio that is forecast at TDM zone level School pupils at the school end are doubly-constrained in the LU model so the TDM receives from it school trip ends rather than merely attractor weights. These are zone matched using school places for the appropriate year group (Cambridgeshire County Council data) and used to doubly constrain school demand in the TDM model. This data is the same data used to generate them in the LU model, so the school trip ends are essentially based on observed school places by TDM zone in For commuting (HBW), a different method is used. Employment data from the Census was largely at Ward level which was not always spatially detailed enough for zone matching, and detailed zone matching also requires a consistent forecasting method. Therefore: commuting trip ends at the work end are constrained to match the LU model across groups of TDM zones corresponding to the LU zones. So the TDM model is always consistent with the LU model but does not enforce an assumption within those areas. each TDM zone and commuting segment M has an attractor size term, which is (attractor for M in t) = (LU arrivals for M in L) * F[t L] where F[t L] = (total commercial floorspace in t) / (total commercial floorspace in L) For example, if L is a Land Use zone with two TDM zone parts and all the commercial floorspace were in one half and the other half entirely residential, this method allocates the work trip ends to the side with commercial floorspace, even though the double-constraint applies to the total across both Floorspace is a standard forecast input to the land use model in the same way as dwellings so this method is consistent in future years. Details of the preparation of zone matching factors in forecast years, is deferred to the CSRM Forecasting Report CSRM Model Development Report 69

78 6 Implementation of the Transport Demand Model +: 855< This chapter documents some of the technical aspects of implementing the demand model, particularly: Parameters for the discrete choice models for all modes The incremental modelling techniques adopted The relationship between the demand model and the SATURN Local Highway Model, both in obtaining cost skims from it and forecasting demand for it Treatment of Goods Vehicles in demand forecasting. +: &&556855= This section documents the key parameters used in the nested logit discrete choice models which are the core of the Demand Model. The mathematical form of these models is as described in TAG unit (DfT 2006), especially appendix 5, and the notation used here is consistent with the notation defined in that appendix. The TAG illustrative parameter values given below are from Section 1.11 of that report The hierarchy used is the WebTAG default hierarchy for all purposes, as discussed in Chapter 2, with three main levels as shown below: main mode choice, macro time of day choice, then distribution. Trip gen. Car PT Walk Cycle AM peak... AM peak... AM peak... AM peak... Dest 1 Dest 1 Dest 1 Dest Figure 6-1: Main levels of choice hierarchy The process by which these parameters were calibrated is discussed in Section The lambda () parameters shown in Table 6.1 are the sensitivity parameters used in tour distribution. For any tour origin i, destination j, main mode m and demand segment p, the trip distribution probability is: 70 CSRM Model Development Report

79 T ijmp T = imp k B exp( λ k j B exp( λ dest, m, p dest, m, p G G ijmp ikmp ) ) (WebTAG A5.1.5) where B j are zonal attraction (size) terms for zone j defined in Section 5.3; Purpose G ijmp is the generalised cost of travel from zone i to zone j by mode m for trip purpose/segment p from the assignment models after sub-mode choice λ is the distribution parameter referred to above; and dest, m, p T imp is the number of trips from i by the mode m after the mode choice step The composite disutility at an origin that is passed up to the TOD and mode choice nests is: U imp = ln B j exp( λ dest, m, pgijmp ) ln B j (WebTAG A5.1.2) j Note that is for a two-way (out+return) cost for home-based purposes and a one-way trip cost for non-home-based purposes. (See Section 2.6 and especially Table 2.13). The illustrative parameter values in WebTAG are understood to be for a one-way trip cost, and have been halved as appropriate for comparison with the homebased purposes below. Also note that they are quoted as positive in this notation. Table 6.1: Distribution parameters for Car and Public Transport, with WebTAG default values per generalised minute of one or two-way trip Mode CSRM WebTAG illustrative values lambda For purpose Min Median Max j % diff from Median Cost units Home-Based HB Work HBEB HB PB/Shop HB Rec/Visit HB Education Car.0260 HBW % 2-way PT.0198 HBW % 2-way Car.0268 HBEB % 2-way PT.0216 HBEB % 2-way Car HB Other % 2-way PT.0216 HB Other % 2-way Car HB Other % 2-way PT.0216 HB Other % 2-way Car.0450 HB Other % 2-way PT.0216 HB Other % 2-way Non-Home-Based NHBEB Car.0648 NHBEB % 1-way PT.0504 NHBEB % 1-way NHB Other Car NHB Other % 1-way PT.0396 NHB Other % 1-way Default values source: WebTAG June 2006 section CSRM Model Development Report 71

80 6.2.7 WSP were advised that forthcoming WebTAG guidance will state that lambda values should be within ±25% of the WebTAG Median lambdas, as the minimum and maximum values are based on a small sample of models. All parameters in the current model are within those 25% bounds. 2 +:" <&3; &#435&& Because of the local importance of cycling in the Cambridge area, the CSRM travel demand model includes both walking and cycling as distinct modes with separate trip distribution models. This means that the model requires distribution parameters for walk and cycle by travel purpose. These parameters include within them the travel speed, the spatial spread of trips and the travellers sensitivity to time on that mode for the mode choice model A local parameter estimation was not possible within the CSRM project, and WebTAG guidance regarding these modes is limited, so it was desirable to transfer distribution parameters for these modes from comparable models that were able to make a statistical estimate. RAND Europe were commissioned to provide a short note about walk and cycle parameters derived in model estimations they have developed, both in the UK and overseas see Fox (2008). This suggested a minimum, maximum and average of parameters from comparable models for each of walk and cycle modes by purpose. These ranges and the parameters adopted by CSRM are listed in Table 6.2. The details of the parameter transfer are further set out in WSP Technical Note TN042 (July 2008) A difficulty that arises when statistically estimating walk and cycle models is that the speed and lambda are highly collinear and it is therefore difficult to estimate both. All of RAND Europe s models estimate them together, in the form of a distance term. In this formulation, rather than estimating time from distance and then applying a lambda to the time, the generalised cost term is replaced by a multiple of distance. The trip distribution probability becomes: T ijmp T = imp k B B k j exp( β exp( β distance, m, p distance, m, p d d ijmp ikmp ) ) where d ijmp is the distance by mode m (walk or cycle) and purpose β is the distribution parameter by mode and purpose dist ance, m, p and similarly the disutility passed up for mode choice is U imp = ln B j exp( β distance, m, pd j ijmp ) ln j B j 2 DfT advised in teleconference on 10 Feb 2009 that the new guidance would recommend using values within +/- 25% of the median values, as the min and max values are based on quite small samples of models, and this applies to all trip purposes. 72 CSRM Model Development Report

81 Table 6.2: Distribution parameters for Walk and Cycle, with RAND illustrative values Purpose Mode distance (per km) Nominal lambda* RAND illustrative distance values (per km) For purpose Min Median Max Cost units Home-Based Walk HBW way HB Work Cycle HBW way HBEB Walk EB way Cycle EB way HB PB/Shop Walk Shopping way Cycle Shopping way HB Rec/Visit Walk Other way Cycle Other way HB Education Walk Education way Cycle Education way Non-Home-Based Walk EB way NHBEB Cycle EB way NHB Other Walk Other way Cycle Other way Bold indicates the number used where applicable * The nominal lambda is the equivalent lambda per generalised minute if the walk speed is assumed to be 4.8 km/h (3 mph) and the cycle speed 10 km/h (6.2 mph). These speed assumptions are not used directly in the model, but these values are presented to facilitate comparison with the car and PT parameters In most segments the median of RAND illustrative values has been used. In the case of HB Rec/Visiting, it is expected that the trips will be longer (based on the NTS) so the minimum beta values have been used For education walk trips, the maximum beta has been adopted to better match the trip lengths of students who walk in the National Pupil Database. +:( 586&4&#85=85&& The theta parameters () in Table 6.3 denote relative sensitivities of different levels of the choice tree (Figure 6-1), in particular [tod/distribution] is the sensitivity of macro time of day choice relative to distribution, and [mode/tod] is the sensitivity of main mode choice compared to time of day choice Non-home-based purposes do not have time of day or mode choice in the current model, though their mode split is partially determined by the mode of the homebased trip from which they are derived. The other exception is HB Education where there is no time of day choice, so there is only a parameter for [mode/distribution] For Home-Base purposes other than education, the relative sensitivity of mode to distribution is: [mode/distribution] = [mode/tod] [tod/distribution] Although this is not directly a parameter of the model for these purposes (as those two levels are not adjacent) it is useful for comparison with the WebTAG illustrative values as in Table CSRM Model Development Report 73

82 6.4.5 It is currently assumed that mode and time of day have the same sensitivity, that is, [mode/tod]=1 for all purposes. Thus [mode/distribution] = [tod/distribution]. Table 6.3: Relative sensitivity of main mode, time of day (TOD) and distribution choice Purpose [mode/tod] [TOD/distribution] [mode/distribution] HB Work HBEB HB PB/Shop HB Rec/Visit HB Education n/a n/a Table 6.4: Relative sensitivity of main mode and distribution, with WebTAG default values WebTAG default values Purpose mode/distribution For purpose Min Median Max HB Work HBW HBEB HBEB HB PB/Shop HB Other HB Rec/Visit HB Other HB Education HB Other Default values source: WebTAG June 2006 section :* 91785= Sub-mode choice nests under distribution choice as described in Chapter 2. The sub-mode choice parameters are shown in Table 6.5 below. They are set to fairly large values on the assumption that they are more sensitive than distribution, and that travellers strongly minimise the observed generalised cost. Table 6.5: Sub-mode choice parameters Choice Lambda Cost units Car vs Park & Ride way Park & Ride site choice way Bus vs Rail/GB way Rail versus GB way Rail station choice, Rail: car access versus non-car access way 74 CSRM Model Development Report

83 +:+ 8&# In most models, using the generalised cost directly in mode split and distribution results in the model s elastic response to fuel price or car time changes being dominated by very long trips in a way that does not seem to accord with actual experience. Other Value of Time experiments have shown, simultaneously, that the marginal influence of both cost and time seems to decrease for very long trips. It is therefore common practice to apply some form of cost damping to long trips in order to reduce the elasticity of response and improve model realism. It is understood that the Department is preparing guidance on this, but that this guidance is not yet available The CSRM model applies a damping function to all purposes with the exception of education which tends to have short trip lengths and the issues do not arise to the same degree. The functional form is * γ U = AU where * U is the damped generalised cost of travel and U is the original generalised cost of travel, between an origin zone and a destination zone. A and are constants depending on trip purpose as in Table 6.6 for car and Table 6.7 for PT For car travel a milder damping has been applied to most purposes (=0.7), except Home Based Recreation and Visiting (HBRV) and Non-Home Based Other (NHBO) which use =0.65 on the assumption that long trips within this purpose are less sensitive to cost. These purposes are known to have a heavy tail of long trips compared to other trip distributions, perhaps reflecting sub-segments such as visiting relatives whose distribution is less affected by cost For PT travel the damping has been set at =0.85 for all purposes where damping is applied For other modes no damping is applied The functional form can be seen as a form of Box-Cox transformation applied to the whole generalised cost function. The coefficient A is calculated so as to make U*=U when U=60 generalised minutes of two-way cost (or U=30 generalised minutes of one-way cost for the Non-Home Based trips): 1 γ A = 60 for HB trips and 1 γ A = 30 for NHB trips This means the damping function is nearly linear for trips shorter than that and so has little impact on the shorter distance trips CSRM Model Development Report 75

84 Table 6.6: Car cost damping parameters by trip purpose Purpose Power Coefficient A HBW HBEB NHBEB HB rec/visiting HB PB/shopping HB Education no damping NHBO Table 6.7: PT cost damping parameters by trip purpose Purpose Power Coefficient A HBW HBEB NHBEB HB rec/visiting HB PB/shopping HB Education no damping NHBO :! &31&8# This section describes how the car and public transport lambda parameters and damping parameters were calibrated. The main considerations were realistic elasticities of response, and that car trip lengths and volumes match those observed in the RSI surveys. The results are given in Chapter Because the model is an absolute model the trip lengths are related to the distribution lambda parameters. (HBW is something of an exception, as described in Section 6.9, and the residual disutility method described in that section matches the observed Census trip lengths). However, it was possible to include a residual disutility term that adjusts trip lengths without adjusting the sensitivity, and this was done for EB, Discretionary and Education purposes so that trip lengths across the RSI cordon matched the observed RSI data First, the model was set up with WebTAG median lambda and theta parameters for car and PT, and with no cost damping of any mode. The car trip lengths were found to be generally good but the fuel cost elasticities were much too high: fuel veh-km and PT fares CSRM Model Development Report

85 6.7.4 Then two different damping parameters were applied, both using the functional form described in Section 6.6. First a power of 0.9 for all car and PT trips and then a power of 0.7 for all car and PT trips. The latter reduced the elasticities to (fuel -0.39, PT -0.21). The trip lengths were not much affected, but the elasticities and pattern of elasticities were much improved. As the damping function used potentially causes some distortion of trip lengths at short distances, and education trips are typically quite short, it was decided not to damp education trips for any mode The next step was to try a range of distribution lambda parameters. As NHB trips are derivative of HB trip ends, it was decided to adjust HB and NHB separately to see how NHB trip generation was affected. Reducing all HB lambdas by 20% gave elasticities of (fuel -0.33, PT -0.21) but with discretionary (HBPS, HBRV, NHBO) elasticities still quite high (average of -0.65). Conversely, education responses seemed quite low, at Based on DfT advice that the latest WebTAG guidance allows lambdas in the range of the median ±25%, HB discretionary lambdas were reduced to median -25% and education lambdas returned to median. This gave elasticities of (fuel -0.30, PT -0.20). Then NHB lambdas were brought into line with HB NHBEB at median -20%, NHBO at median -25% which gave (fuel -0.29, PT -0.20). These results were discussed with DfT At this point it was decided that fuel elasticities were nearly right, but PT elasticities looked quite low (-0.2 is the minimum recommended by WebTAG). Increasing PT lambdas to WebTAG median +20% was tried, but the effect on elasticities was small, as the correcting for the changes in the base mode split largely cancelled out the change in response The discretionary purposes HBRV and NHBO had very large fuel cost elasticities associated with long car trips outside the study area, so their damping was made stronger. After some experimentation, reducing the power term from 0.7 to 0.65 gave acceptable looking results with an overall fuel price elasticity of This gave the final set of car cost damping coefficients in Table As trip lengths and mode split within the model had drifted with these parameter changes, it was decided to do some recalibration. Improving the education mode split brought about a higher PT elasticity for education. Some adjustments were made to car time of day and distribution based on the RSI (via additional disutility terms, which are equivalent to K-factors). Trip generation in external zones was improved for commuting, EB and discretionary trips based on RSI and new Atkins prior matrices (which use East of England Regional Model outputs). At the end of this process, the overall elasticities had moved to (fuel -0.27, PT -0.26) and were discussed with DfT again The main remaining realism issue was that the PT fare elasticities looked low. After carefully investigating the behaviour of the PT model, the following corrections were made: Public Transport damping was changed to use a power of 0.85 for non-education PT, which increased the elasticity for HBW and EB where some travellers take long rail journeys. After checking the results, this gave the final PT damping parameters shown in Table CSRM Model Development Report 77

86 Increases to external PT attractors for HBRV so that they represent the population of external zones relative to the study area. (eg. the attractor for London divided by the total attractors for the study area is now the population of London divided by the population of the study area). This produced longer rail journeys in the HBRV discretionary purpose, which were also more consistent with the trip lengths in the National Travel Survey for that purpose. Adjustments to the bus/rail mode specific constants based on LATS rail boarding data showing the model had too much rail use overall This produced the final set of elasticities shown in Table 7.6 and Table :- 855< 86#55#&38533#0&8&= The CSRM model includes two independent and complementary incremental methods. The first is pivoting of the LHM highway matrix, which is applied to all light vehicles for all purposes as described in Section 6.12, but only at the Origin-Destination (O-D) level by time period The second technique is residual disutility (RD) modelling which helps to calibrate the shape of the base matrix and mode split patterns based on observed base year data. This was used in calibration of HBW car and public transport trips. Technical problems prevented it being used for education in the current model, and there was no suitable data available for other purposes. RD in this case is always applied at the Production-Attraction (PA) level This approach to incremental modelling takes the synthetic matrix output from an absolute choice model and compares it to a target (observed) matrix. The programme used then calculates the additional term that needs to be added to the modelled generalised cost (disutility) in order that the modelled synthetic matrix will match the observed target matrix The resulting generalised cost term for the zone pair / demand segment is then input to all future model runs as an alternative specific constant in exactly the same way as a mode specific constant. In fact the residual disutility approach is very much like calibrating a mode choice model with a mode specific constant, except that here it is applied to the distribution model also The residual disutility based approach has the following benefits: An absolute model can be created in advance of base matrices of modal data being available and the realism of the results obtained from this absolute model to be checked. Incremental modelling can be applied at the individual zone pair level or at a more aggregate sector pair level where the size of the sectors can be specified by the user based on the quality of the data available. Incremental modelling can be applied to partial modal matrices (ie leaving some zone pairs unconstrained) based on the full range of data available, without the need to first combine the datasets into comprehensive base year matrices. Straightforward inclusion of new development zones in forecasting years in the absolute model. That is, the RD approach naturally extends to absolute demand modelling in zones for which there is no base year demand to pivot from. 78 CSRM Model Development Report

87 Can also improve the realism of Public Transport assignment matrices; given that there was no data with which to estimate a base year matrix. In the present version this is limited to education trips but could be extended to HBW public transport in future versions Further information on residual disutility based modelling is available in TN35 Specification of WebTAG Default Transport Demand Model for Cambridge TIF In particular for Cambridge the 2001 Census of Population provides information at the P-A level for all modes of travel as implemented in the distribution model which when used to convert to incremental form should greatly improve the representation of local travel in the model for these purposes. +:' &&68#55#&38533# For HBW, the data used to set up RD incremental modelling was the Customised and standard published tabulations of the Census Journey to Work (JTW) data for those living or working in the study area The Census matrices are used to control the trip ends by mode, and the P-A trip matrix for car trips, and the P-A matrix for Public Transport trips. This data is all by Census ward, so all RD adjustments are at ward level rather than individual zone level, using the flexibility of the RD approach. Where matrix values are small (0 to 3 persons) extensive noise is introduced into the data by ONS randomisation procedures, so it was necessary to use this data carefully. This was a more serious problem with the public transport matrices than the light vehicle matrices, hence a somewhat more aggregate zoning was used for the PT matrix control Additional sets of data available to help calibrate the CSRM included: Roadside interview (RSI) data for trips crossing cordons in and around Cambridge City and around Huntingdon, providing limited traveller type data, but does provide person / light vehicle movements for a 12 hour period by direction (from / to home and non home based). Base highway matrices estimated from a mixture of the RSI, NPD and JTW datasets and calibrated to link count data, in the form of O-D trip matrices for three one hour periods: AM, IP and PM peak. This dataset retains information on trip purpose but excludes information on the direction of the journey. This was used in O-D pivoting, and to derive exogenous trip ends for EB and discretionary purposes in external zones in the base year. Extracted pupil data from the National Pupil Database (NPD) for those pupils living or at school in the study area; These datasets were useful in calibrating mode choice and other aspects of model calibration but were not used as RD incremental inputs directly. The RSI sample is too small, and the estimated matrices are O-D not P-A CSRM Model Development Report 79

88 6.9.5 The National Pupil Database (NPD) is an administrative dataset set up and used for the purposes of Department for Children, Schools and Families (DCFS) and used here by permission. Its primary use in DCFS is to track the educational progress of students, but it also records the home and school location of all pupils in all primary and secondary schools except independent schools. It is updated every year from the Pupil Level Annual School Census in which all government-aided schools must participate, so it is a nearly full census of the production-attraction matrices of school travel. Data was received from DCFS for the academic year 2006/2007, and in slightly different form from Cambridgeshire County Council for 2006/07 and 2007/ The data records the home location for virtually all the pupils, but only has the mode of travel for 74% of the pupils. In total 40% of the trips are known to be made by walk / cycle while only 19% are known to be made by car These matrices record the school and home locations of pupils using postcode information which has been converted into the transport model zoning system using GIS functionality. Thus the data is in principle available at the zone pair level for incremental modelling In practice there were difficulties in getting the RD method to converge and at present education is not RD modelled. This may be to do with the difficulties of accounting for the independent schools any zone with an independent school covering a certain age had to be omitted. Or it may be to do with the lack of complete modal data, which meant the RD had to be attempted across all modes, which is more difficult as it interacts with mode choice. Finally, it might be to do with the lack of modelling of dedicated school buses in the current model To conclude, there may be scope in future to improve the modelling of education using the NPD, but at present it is not included Detailed documentation may be found in WSP Technical Note 40a (July 2008), and a worked example illustrating the possible improvement in elasticity results was issued as WSP Technical Note 48 (September 2008). 80 CSRM Model Development Report

89 +:, 3#; 15< 55#&#85&#853&#38&3 =0=<&4853 Previous Year Iteration 1 Iteration 2 Costs Demand LU (trip ends) Starting Costs Transport Demand Transport Demand PT-Walk-Cycle Assignment PT-Walk-Cycle Assignment PT-Walk-Cycle Assignment Convergence Test P&R car legs Growth Factors Growth Factors SATURN HW Demand X X SATURN Highway Model SATURN Highway Model SATURN Highway Model Figure 6-2: Model structure, showing relationship between the Transport Demand Model and the SATURN Local Highway Model There is a two-way flow of information between the MEPLAN software implementing the TDM and the SATURN software implementing the LHM From the SATURN assignment model to the Demand model: LHM matrices of congested journey distances and times by user class for the three assigned one hour periods are read directly into the transport demand model. TDM constructs cost matrices based on the distance matrices from SATURN and pence per kilometre cost values. Generalised costs of travel are constructed from these matrices by the demand model for the different traveller types using appropriate values of time From the Demand model to SATURN assignment model: Final synthetic highway matrices in O-D format are output from the demand model for modelled periods: 3 hour AM and PM peak periods and 6 hour interpeak period. The ratio of the forecast matrix to the base matrix for a given period is used to scale the base highway O-D matrices These steps are iterated as shown in Figure 6-2, starting with highway assignment, then using the costs in the demand model. The demand outputs are used to pivot the SATURN matrices (this step is marked X ) and assigned, and the process then repeats CSRM Model Development Report 81

90 The next few sections describe some of the details of these linkages. +: SATURN skims are obtained using the standard utility SKIMALL. This builds minimum generalised cost paths for each inter-zonal zone pair through the network resulting from a converged SATURN assignment, and output the distance in metres, congested time in seconds and any Road Pricing Toll charge in pence. The results consist of 24 distance matrices and 24 time matrices for each of the 8 light vehicle user classes and 3 time periods as well as 8 road user charge skim matrices for the AM Peak (the matrices for the other time periods being discarded as there are no scenarios in the CSRM model where road pricing is charged outside of the AM Peak). In road pricing scenarios, these toll skim matrices are then processed for further use The Demand model distances are then built up from these in a straightforward way: TDM distance TDM cost = skimmed distance / 1000 metres per km = (TDM distance * PPK[user class, time of day] + skimmed road user charge) / Occupancy[segment] where PPK = Pence Per Km is an input to the model TDM time = skimmed time / 60 seconds per minute + terminal times TDM generalised cost = TDM cost / Value of Time[segment] + TDM time Terminal times are additional times per zone for the origin and destination representing the time required for parking etc. as discussed below The two models have slightly different zoning systems, because the LHM has split a number of the demand model zones into two in order to improve the highway assignment. The LHM also has separate zones for current and future Park & Ride sites which the demand model does not use. So the 325 zones in the current LHM all nest within the 306 Demand model zones. Further details about the zoning system may be found in Section 2.2 and Appendix B zone skims for the demand model are obtained by taking distances etc. between representative zones in the 325-zone system, rather than using weighted averages. This is straightforward because wherever a Demand model zone has been split there is an LHM zone with the same number that is the best representative for the purpose of obtaining skims. For instance, TDM zone 1469 in the LHM is split into zones 1469 and 1495, but zone 1469 includes the main settlement (Fen Drayton) from the original zone. So re-zoning the skims simply consists of taking the rows and columns from the 325-zone skim with the same number as the demand model zone and discarding the remaining rows and columns The car leg of P&R trips are represented in the PT, walk and cycle network as links from various zones to the P&R sites. (Some Cambridge city zones cannot be the car end of a P&R trip as shown in Figure 3-9). The distances and times of these links are updated based on the SATURN skims to and from the dedicated P&R zones in the LHM A few other terms are added to the skims obtained from SATURN before they are used in the demand model: 82 CSRM Model Development Report

91 Intrazonal matrix entries are added as described in Section 3.8. Some adjacent zone pairs where SATURN does not provide a good distance or time skim are replaced with exogenously determined values. In zones where SATURN centroid connectors are coded as simulation centroid connectors (Van Vliet et al. 2008, Section 5.1.8), the vehicles are assumed to appear on a chosen link with no additional distance between the centroid and link. In a few zones the centroid is some distance from the highway network and this appreciably reduces the effective highway distance to and from the zone, so some additional distance, cost and time are added for this leg. (This is typically in rural zones where the centroid represents the true centre of zonal population, but where the nearest LHM nodes are several kilometres from the centroid or minor roads not in the LHM network must be used to access the main road network. Parking charges and additional times related to parking and unparking the car are added depending on the zone at the non-home end of the trip or the destination for NHB trips. Excluding city centres, these are typically 2.5 minutes at each end, so the car mode has a fixed time of 5 minutes relative to walk, cycle and PT modes. This helps represent the overhead of using a car especially for very short trips. The innercity values are higher, particularly as in central Cambridge it is often difficult to find parking and not possible to park in exactly the destination zone so there is an additional time required to walk from a parking location. Including this time is essential if the competition between car, Park & Ride and other modes is to be modelled successfully. +: 8#5#07=8958#&585&; =89=0=<&4& This section describes how a synthetic light vehicle trip matrix (or synthetic highway matrix) is extracted from the larger demand model matrices. There are three parts: conversion of person trip matrices to vehicles, including only trips with a private car leg conversion of 3 or 6 hour demand to peak hour demand for the one-hour periods modelled by the LHM extraction of car legs from end-to-end Park & Ride and rail trips with a car access leg ( Park & Ride for rail) The model assumes car trips have a fixed occupancy by purpose and time period as shown in Table 4.2, and the same occupancies are assumed for the car legs of P&R trips etc. so the vehicle demand is just the person trip demand divided by these constants Fixed ratios are also used to convert 3-hour to 1-hour in the peak periods, and 6-hour to 1-hour in the interpeak. These are based on the ratios of vehicles by purpose in the RSI interview data, as shown in Table The car leg extraction involves moving either the origin or destination of a P&R trip or Rail with car access trip to the relevant zone, which will be one of the LHM s Park & Ride zones (see Table B.2: in Appendix B) or the zone containing the relevant Rail Station CSRM Model Development Report 83

92 This synthetic highway matrix is the form of the synthetic matrix used for pivoting the SATURN Base matrix. Table 6.8: N-hour to 1-hour vehicle trip ratios AM: 7:00-10:00 to 8:00-9:00 IP: 10:00-16:00 to 14:00-15:00 PM: 16:00-19:00 to 17:00-18:00 HBW EB Education Other :" 8#0=5=0=<&4&? The TDM produces synthetic highway matrices (see previous section) based on modelled trip ends (from the Land Use model) and mode, time of day and destination choice models. However, these matrices are not suitable for highway assignment by themselves, so CSRM uses a matrix pivoting procedure that updates the validated base LHM matrix using differences between the synthetic TDM matrices This section describes the pivoting procedure implemented in the CSRM model at the time of writing and used for conducting elasticity tests in the base year The pivoting (or scaling ) of the base highway matrix uses the ratio between the synthetic matrices and the base year matrix to produce a forecast LHM matrix for assignment. Pivoting is implemented for the three time periods at the individual 325-zone pair level and separately for each of the 8 light vehicle user classes in the local highway model A forecast LHM matrix F is to be prepared according to the following requirements, based on four input matrices: B, a base year LHM matrix of dimension (325 x 325 x 10 UC) X, a base year synthetic TDM matrix which will be aggregated to match the 8 light vehicle LHM user classes it models. So X has dimension (306 x 306 x 8 UC), though for convenience we may extend it to 10 user classes with zeros for the 2 HGV User Classes. Y, a forecast synthetic TDM matrix, also (306 x 306 x 8 UC) E, a forecast exogenous matrix, containing heavy vehicle matrices and through trips by light vehicles from East of England Regional Model forecast runs. (325 x 325 x 10 UC) Let B[o,d,uc] denote an element of the 3-dimensional matrix by origin, destination and user class. Then the basic pivoting equation is to use the growth between X and Y to scale B, and bring in the exogenous trips: F[o,d,uc] = B[o,d,uc] * (Y[o,d,uc] / X[o,d,uc]) + E[o,d,uc] Because of the difference in zoning systems, we need to do this in two steps. Since the synthetic matrices have 306 zones, they must be extended to 325 zones. This is done by: 84 CSRM Model Development Report

93 moving the car trip endpoints of synthetic Park & Ride trips and Rail with car access trips from the TDM true trip origin / destination zone to the appropriate park and ride site or Rail Station. So either the origin or destination as appropriate is changed to the appropriate zone in the LHM. splitting zones which consist of multiple LHM zones, effectively assuming the same growth factor for each constituent LHM zone Let X and Y (325 x 325 x 8 UC) be the synthetic matrices after this process. Then we actually have a growth rate and G [o,d,uc] = Y [o,d,uc] / X [o,d,uc] (325 x 325 x 8 UC) F[o,d,uc] = B[o,d,uc] * G [o,d,uc] + E[o,d,uc] If X[o,d,uc] is zero, we set G to zero. This will happen for through trips or goods vehicle trips where the matrix cell should come from matrix E anyway, and the modification of X below should prevent it for the remaining internal zone pairs. To avoid runaway growth if X is small and Y is not, G is capped at It was found that when zones were combined into Sectors this process of scaling at the zonal level could result in unbalanced growth (i.e. that for a given aggregate origin and destination F/B for a given UC may not be the same as Y/X). It was decided to check the growth rates for these aggregate origins and destinations and where necessary introduce a corrective factor to be applied to all cells F[o,d,uc] where o is in the origin sector and d is in the destination sector. The value of uc is the SATURN user class for which the growth between this origin sector and destination sector has this specific imbalance. Where the growth from B to F should come from matrix E we override the calculation and decree that the corrective factor for these cells is In the base year run there is no iteration of the demand model with the SATURN highway assignment model since the highway costs are based on the validated base highway matrix. However pivoting is required in the base year (2006) to perform realism tests such as increasing fuel cost 10%, so that the congested assignment is brought into equilibrium with the demand changes Before assignment in SATURN, the pivoted matrix is smoothed by producing a weighted average with the previous iteration (see para ). +:( 0885=355&# Heavy Goods Vehicles (HGVs) and Light Goods Vehicles (LGVs) used primarily for goods transport are not modelled in the demand model. Light Goods Vehicles used for passenger transport are included in the demand model and LHM matrices for each trip purpose/user class in the same way as cars. 3 The specification of the pivoting algorithm was originally described in WSP Technical Note TN044 (July 2008) CSRM Model Development Report 85

94 In the base year, goods vehicles were included in the Road Side Interviews (RSIs) and are therefore captured in the highway matrix estimation process, as described in the LHM documentation. HGVs have two user classes (one is to do with local access in Huntingdon). A significant proportion of LGVs will be in the Employer s Business (EB) User Class. So base year GV demand for highway capacity is fully represented by this matrix of PCU-equivalent trips, although LGVs are not separated by trip purpose Table 1 of the DfT Van Activity Report 2004 confirms that 50% of noncommuting van activity relates to goods delivery. The relevant numbers from that report are shown in Table 6.9. In this analysis, commuting related van activity (home to work, home to employers business) is ignored. Table 6.9: LGV non-commuting EB van activity Purpose LGV Veh-Km (million) LGV trips (million) Delivery of goods Collection of goods Collection and delivery of goods Travelling between jobs Empty travel Other business use Proportion of collection/delivery of goods-activity in EB LGV activity Source: DfT, Van Activity Report % 33.6% Based on the figures given in Table 6.9 and grouping of activities that relate to collection or delivery of goods together, van usage is 50% of the LGV EB vehicle-km and 33.6% of the LGV EB trips totals The analysis of the time of day based on manually classified traffic count (MCC) records and Cambridge RSI surveys (Atkins, 2006) shows: The proportion of flow that is LGVs, The proportion of trips that are Employer s Business, for LGVs and for all light vehicles It is assumed that these RSI cordon crossings are a fair sample of traffic this is not going to be totally correct, but in the absence of any other data the assumption must serve for this purpose. Then supposing 33.6% of LGV EB trips are for collection/delivery in each time period, it is possible to estimate a proportion of EB vehicles that are LGV trips for goods collection/delivery. The numbers are shown in Table 6.10 below. 86 CSRM Model Development Report

95 Table 6.10: Estimating goods delivery as proportion of LGV trips, by time period A B C D E F Estimated quantity LGVs in total flow (Cambridge MCC) Proportion of LGVs that are EB (Cambridge RSI) Proportion of LGV trips in goods delivery/collection (van survey) LGV goods delivery/collection as proportion of total vehicles = A*B*C EB trips as proportion of total trips (Cambridge RSI) LGV goods delivery/collection as proportion of EB vehicles = D / E AM Peak Inter peak PM Peak 7:00-10:00 10:00-16:00 16:00-19: % 12.7% 8.0% 38% 69% 24% 33.6% 33.6% 33.6% 1.33% 2.94% 0.64% 14.2% 25.4% 10.6% 9.5% 11.8% 6.0% The final line F in Table 6.10 is the percentage of vehicles that the TDM (passenger) model would expect to be missing when compared to the RSI data or the EB user class in the LHM matrix. Such goods delivery LGVs are expected to be around 9%, 12%, and 6% of the EB trips in AM, IP, PM respectively. Given the uncertainty in this estimation, we might conclude that the demand model is expected to produce around 10% fewer trips than the RSI or the LHM The CSRM model does not currently include a freight demand model, so HGV demand is an entirely exogenous input, and LGV demand forecasting requires an additional growth assumption regarding LGVs carrying goods. The forecasting of these vehicles is described in the CSRM Forecasting Report. The current proposed approach is to apply a separate growth trajectory to a proportion of the EB user class There is no demand response modelling of the exogenous parts of GV demand except for possible re-routing within the LHM network. This includes all HGVs and the LGV through trips. These parts of the demand matrix have no representation of mode, time of day or distribution changes in response to cost changes. In practice few of these trips do have much choice as alternative routes in / out and through the study area are limited. For the purpose of testing pricing scenarios, the volume of HGVs in Cambridge in the AM peak is extremely small, so representing time of day switching for them was not necessary CSRM Model Development Report 87

96 +:* 8#505# Convergence is measured using the % Gap statistic as defined in TAG unit Section 1.5: G = ijctm C( X ijctm ijctm ) D( C( X C( X ijctm ijctm ) X )) X ijctm ijctm where i is the origin j is the destination c is the demand segment t is the time period m is the mode *100% X is the synthetic demand matrix. X ijctm is the element of the demand matrix associated with the given variables. C(X) is the generalised cost matrix obtained by assigning X, ie. the generalised cost derived from the SATURN cost skims. D(C(X)) is the demand matrix obtained using the costs C(X), ie. the demand from the subsequent iteration of the TDM Note that the CSRM % Gap includes walk and cycle modes, whereas some other models do not model demand for these directly Also for simplicity the % Gap is based on the synthetic highway demand. So X and D(C(X)) are the synthetic matrices from the TDM and C(X) are the costs obtained when the pivoted version of X was assigned. However, the nature of the pivoting process is such that if car mode matrix cells in successive iterations X ijctm, D(C(X ijctm )) differ by k % then the pivoted LHM matrices produced using them will be differ by k%. So if the % Gap statistic indicates that the synthetic demand matrix has converged, then the pivoted LHM matrix will also have converged To improve the rate of convergence a smoothing process has been introduced between iterations. The smoothing is applied to SATURN highway volumes, after the pivoting process and before SATURN assignment. By default, the assigned SATURN matrices are a 50:50 average of the current and previous iteration pivoted values, for the second to fourth iterations. After this point we use a 20:80 average of the current and previous iteration pivoted values. 88 CSRM Model Development Report

97 7 Demand Model Validation!: 855< This section contains CSRM Transport Demand Model Validation results, representing the most definitive and up to date comparisons of CSRM with RSI and other non-lhm data sources. All model results presented are based on a version of CSRM using SATURN LHM v10 (provided 15th July, 2009) Presented here are the results most relevant to the overall patterns of demand within the model. This covers a comparison of the synthetic highway demand with the Road-Side Interview data, validated SATURN LHM matrices, mode and time of day split, fuel cost and PT fare elasticity responses and trip lengths. Public transport validation is presented in Chapter 8.!: &&&&3& The data available for validation of the transport demand model is as follows: Specially commissioned ONS 2001 Census tables of journeys to work by mode have been used as part of the incremental modelling. These tables provide a detailed breakdown of mode of travel to work. The comparison of commuting mode share and car availability is presented in Section 7.3. National Pupil Database data for the area records the mode of travel to school by pupil, for each state school in Cambridgeshire. This is used to compare the mode share for education trips in Section 7.3. The CCC 2006 Roadside Interview Data provides a range of information on car and light vehicle trips which was used in the SATURN LHM validation. They are used here to validate the volumes, trip lengths, and time of day split for the parts of the trip matrix which the RSI was able to sample. The time of day information by trip purpose has been compared against CSRM results in Section The SATURN Local Highway Model (LHM) is validated against Road-Side Interviews and observed vehicle flows. The LHM demand matrices are segmented by AM peak, interpeak and PM peak hours, and into 8 user classes. It is important that these matrices are sufficiently consistent with the synthetic demand model in order for the model to forecast highway demand in a consistent way.!:" 85&#586& Table 7.1 compares commuting mode share from a 2001 run with statistics from the 2001 Census. The Census data is based on customised tables received for the CSRM study area from the ONS. This data allows a cross-tabulation of mode and household car availability. 4 All model parameters and results presented in the report related to CSRM run T CSRM Model Development Report 89

98 Table 7.1: Commuting mode share - CSRM vs Observed Data (both 2001) Car Car PT Walk + Other * Cycle availability CSRM ONS CSRM ONS CSRM ONS CSRM ONS No Car 19% 19% 22% 18% 29% 32% 30% 31% Part Car 53% 55% 8% 9% 22% 20% 16% 16% Full Car 75% 75% 4% 4% 16% 17% 5% 4% ALL 66% 66% 6% 6% 19% 19% 10% 9% * Includes Working at or From Home The mode share for education from a 2006 run of the model has been compared against the National Pupil Database made available for the study area. The 2007/2008 NPD has been used, as the percentage of pupils who have a mode of travel recorded is higher than in the 2006/2007 data. Otherwise the mode shares in the two databases are similar, especially given the smaller size of usable sample in 2006/2007. The survey question regarding mode of travel is relatively new and not recorded for all pupils. The results are shown in Table The NPD mode shares are percentages of those records where a mode was recorded. The percentage of the total sample where this was the case is also shown in the table. Some schools make more of an effort than others to collect this data, which explains the variation in reporting by district. In most cases there are 80-95% returns on this question, which is adequate for a comparison. Unfortunately for Sixth-Form (16-18 year olds), in Cambridge district and East Cambs the colleges did not provide data to the NPD, and the returns in Hunts and East Cambs for colleges that did participate are only 68%. (They have been combined because of the small sample). This is compared like-with-like for those two districts below Analysis has shown that the trip distances of pupils with no modal information are similar to the average, suggesting that there is little bias by distance. However, there could still be a bias by mode in whether this question is answered (for example, schools may have better information on pupils who arrive by school bus). Also, the database only records the usual mode of travel and does not register a frequency Finally, the NPD data does not include independent schools, which might be expected to have a larger share of car trips Therefore the error in the NPD percentages cannot be clearly determined but is likely to be large, at least several percent. The model has been calibrated to approximately match but has not been forced to an exact match The mode splits are shown by district, because it is clear that Cambridge has a much higher proportion of pupils cycling, and the model required a Cambridge-specific modal constant to account for this. 90 CSRM Model Development Report

99 Table 7.2: Education mode share by school district 2006 Model vs 2007/2008 National Pupil Database Age and district of school NPD pupils with mode % of NPD with mode NPD mode share (of those with reported mode) Model mode share Age 5-11 Car PT Walk Cycle Car PT Walk Cycle Cambridge 5,168 78% 21% 6% 51% 22% 24% 5% 51% 20% East Cambs 5,770 95% 32% 21% 43% 4% 31% 8% 56% 5% Huntingdonshire 12,466 86% 27% 11% 56% 6% 32% 7% 57% 5% South Cambs 10,010 83% 29% 18% 45% 8% 38% 10% 46% 6% Study Area 33,414 85% 28% 14% 50% 9% 32% 8% 53% 8% Age Cambridge 1,782 51% 6% 21% 33% 39% 5% 22% 40% 33% East Cambs 2,584 84% 15% 60% 23% 3% 31% 28% 35% 7% Huntingdonshire 6,435 82% 14% 30% 44% 12% 21% 23% 48% 7% South Cambs 3,966 81% 14% 56% 25% 6% 26% 39% 31% 4% Study Area 14,767 77% 13% 41% 34% 12% 21% 28% 39% 12% Age Cambridge no data 31% 27% 23% 32% East Cambs no data 9% 13% 72% 9% Hunts & S. Cambs % 21% 28% 39% 12% 38% 17% 34% 12% Study Area sample too small 32% 23% 30% 34% Source: National Pupil Database, Academic Year 2007/2008 Key: differences between model and NPD, as percentage of total trips in District Difference 10% of total 46% Difference > 4% of total 20% Difference < 4% of total 11%!:( &888# This section presents comparisons of car trips in the model versus the RSI. The RSI data is for inbound trips only, as outbound trips were not interviewed at most sites. The observations have been cleaned to remove duplicates and scaled up to match traffic counts, but have not been in-filled in any way or combined with reversedirection outbound data. It includes the Cambridge outer and Huntingdon cordons and the River Cam screenline For this purpose the RSI observations have been filtered to include only zone pairs that would definitely be captured by the cordons. For example, the RSI contains a few trips from south Cambridge to areas outside Cambridge that were captured by the river screenline. Such trips might or might not cross the cordon depending on the driver s choice of route, so that it is not consistent to compare them against the demand model s O-D matrices. Only a small number of observed trips are filtered out in this way The model data is also filtered to the same set of zone pairs. So the model volume is not a set of link loads (as the synthetic matrix is never assigned) but a total across that subset of the matrix which is like-for-like comparable to the filtered RSI CSRM Model Development Report 91

100 7.4.4 The model data in this section is entirely 1-hour light vehicle trips, from the synthetic highway matrix described in section This is created from the 3- and 6- hour demand periods using assumed vehicle occupancies and RSI-based 3/6 hour to 1- hour trip ratios by purpose. This is the matrix used for pivoting the LHM so it is desirable that the volumes in each are consistent. Also the Cambridge P&R sites are outside the RSI outer cordon, so car trips by P&R do not generally cross it. This synthetic matrix contains only the car leg of P&R trips, which again should make them more comparable with the RSI Results are presented both for the entire filtered matrix ( all ) and for internalinternal trips, ie. those zone pairs with both ends within the study area Vehicle trips across the cordon are shown in Figure 7-1. The numbers may be found in Appendix E, Table E.1. Most of the matches are reasonably close. The exception seems to be Education in the AM Peak, though the number of trips represented are only a small proportion of the total AM trips. The poorer match here is related to the modelling of school buses The Employer s Business purpose in the RSI includes Light Goods Vehicles engaged in a variety of activities. The demand model trip rates are based on the National Travel Survey which should include passenger business trips in vans, but exclude professional delivery drivers. That is, the demand model does not attempt to include LGVs which are primarily delivering goods rather than engaged in passenger transport As such, (LGVs engaged in goods delivery are expected to be around 10% of the EB trips,) the demand model is expected to produce around 10% fewer trips than the RSI for that purpose see Section Table 7.3 suggests that we are approximately 11% under the RSI figures for this purpose The Interpeak and PM peak volumes are much smaller because the RSI only sampled inbound movements. 92 CSRM Model Development Report

101 12,000 10,000 1-hour vehicles 8,000 6,000 4,000 2,000 0 AM IP PM AM IP PM AM IP PM AM IP PM HBW EB Education Other Purpose Hour RSI All Model All RSI Internal-Internal Model Internal-Internal Figure 7-1: Car Trip volumes inbound across RSI cordons: CSRM vs RSI by purpose and time of day (Note: Internal-Internal refers to trips with both start and end points within the Study Area) The mean and standard deviation of the trip length for the same set of inbound trips is plotted in Figure 7-2. The bars show the mean trip length, and the whiskers show one standard deviation either side. (Some standard deviations are larger than the mean, because these are distributions with long tails). The numbers are tabulated in Appendix E, Table E CSRM Model Development Report 93

102 AM RSI All Purpose Hour Other Education EB HBW IP PM AM IP PM AM IP PM AM IP Model All RSI Int-Int Model Int-Int PM Trip length (km) Figure 7-2: Mean and standard deviation of car trip lengths, inbound across RSI cordons: CSRM vs RSI by purpose and time of day The trip volumes, lengths and standard deviations are summarised in Table 7.3 below. These are the sum of the 3 hours (AM, inter-peak and PM peak hour) without any weighting. The disaggregate results by hour are deferred to Appendix E, Table E The only really significant variation in the figures is the relative lack of externalinternal trips for Education. Trips from external zones are largely an exogenous input to the model (either from the East of England regional model, or via the LU model). 94 CSRM Model Development Report

103 Table 7.3: Volumes, mean and standard deviation of trip length, inbound across RSI cordons: CSRM vs RSI by purpose, 3 hour total AM + IP + PM Trips (3 hour vehicles) Mean trip length (km) Std. Deviation (km) Purpose RSI Model % diff RSI Model % diff RSI Model % diff HBW Int-Int 11,664 11,463-2% % % Ext-Int 3,764 3,679-2% % % All 15,428 15,142-2% % % EB Int-Int 3,101 2,666-14%* % % Ext-Int 1,355 1,313-3%* % % All 4,456 3,980-11%* % % Educ Int-Int 2,177 3,014 38% % % Ext-Int % % % All 2,623 3,070 17% % % Other Int-Int 8,363 8,452 1% % % Ext-Int 1,681 1,540-8% % % All 10,044 9,992-1% % % Source: RSI cleaned and scaled to counts, filtered to comparable set of zone pairs. (See explanation above). Int-Int: zone pairs with origin and destination in study area Ext-Int: zone pairs with external origins only. Red indicates negative differences (model less than RSI) * EB volumes are expected to be around 10% lower than the RSI, because the demand model does not include LGVs engaged in goods transport CSRM Model Development Report 95

104 To better understand the mean and standard deviations shown in Figure 7-2, the volume of trips by distance band are plotted below. These are the same subset of the matrix ie. trips inbound that would definitely be sampled by the RSI. Again, internal to internal zone pairs are shown with dotted lines, and the totals with solid lines. 1-hour car trips 3,000 2,750 2,500 2,250 2,000 1,750 1,500 1,250 1, <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi 200 mi+ < km+ Distance band RSI inbound Int-Int Model inbound Int-Int RSI inbound all Model inbound all Figure 7-3: HBW, AM peak hour trip length, inbound across RSI cordons 1-hour car trips <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi 200 mi+ < km+ Distance band Figure 7-4: HBW, Inter-peak hour trip length, inbound across RSI cordons 1-hour car trips 1, <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi 200 mi+ < km+ Distance band Figure 7-5: HBW, PM peak hour trip length, inbound across RSI cordons 96 CSRM Model Development Report

105 500 1-hour car trips <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi < mi km+ Distance band RSI inbound Int-Int RSI inbound all Model inbound Int-Int Model inbound all Figure 7-6: EB, AM peak hour trip length, inbound across RSI cordons hour car trips <1 mi <2 mi <3 mi <5 mi <10 mi <15 mi <25 mi <35 mi <50 mi <100 mi <200 mi 200 mi+ <1.6 km <3.2 km <4.8 km <8.1 km <16.1 <24.2 <40.3 <56.4 <80.5 < < km+ Distance band Figure 7-7: EB, Inter-peak hour trip length, inbound across RSI cordons hour car trips <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi < mi km+ Distance band Figure 7-8: EB, PM peak hour trip length, inbound across RSI cordons CSRM Model Development Report 97

106 1-hour car trips <1 mi <2 mi <3 mi <5 mi <10 mi <15 mi <25 mi <35 mi <50 mi <100 mi <1.6 km <3.2 km <4.8 km <8.1 km <16.1 <24.2 <40.3 <56.4 <80.5 < Distance band RSI inbound Int-Int Model inbound Int-Int RSI inbound all Model inbound all Figure 7-9: Education, AM peak hour trip length, inbound across RSI cordons 1-hour car trips <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < Distance band Figure 7-10: Education, Inter-peak hour trip length, inbound across RSI cordons hour car trips <1 mi <2 mi <3 mi <5 mi <10 mi <15 mi <25 mi <35 mi <50 mi <100 mi <1.6 km <3.2 km <4.8 km <8.1 km <16.1 <24.2 <40.3 <56.4 <80.5 < Distance band Figure 7-11: Education, PM peak hour trip length, inbound across RSI cordons 98 CSRM Model Development Report

107 600 1-hour car trips <1 mi <1.6 km <2 mi <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi 200 mi+ < km+ Distance band RSI inbound Int-Int Model inbound Int-Int RSI inbound all Model inbound all Figure 7-12: Other/discretionary, AM peak hour trip length, inbound across RSI cordons 1,400 1,200 1-hour car trips 1, <1 mi <2 mi <3 mi <5 mi <10 mi <15 mi <25 mi <35 mi <50 mi <100 mi <200 mi 200 mi+ <1.6 km <3.2 km <4.8 km <8.1 km <16.1 <24.2 <40.3 <56.4 <80.5 < < km+ Distance band Figure 7-13: Other/discretionary, Inter-peak hour trip length, inbound across RSI cordons 1,200 1-hour car trips 1, <1 mi <2 mi <1.6 km <3.2 km <3 mi <4.8 km <5 mi <8.1 km <10 mi <16.1 <15 mi <24.2 <25 mi <40.3 <35 mi <56.4 <50 mi <80.5 <100 mi < <200 mi 200 mi+ < km+ Distance band Figure 7-14: Other/discretionary, PM peak hour trip length, inbound across RSI cordons CSRM Model Development Report 99

108 !:* &586& The model s allocation of the 12 hour 2006 car trips into time periods has also been compared with the raw RSI data at a more detailed purpose split, and without the filtering of zone pairs described above. (This is the 12-hour RSI data, not just the LHM s peak hour periods.) There is potentially a larger error term in the RSI when used in this way, as it is only a sample and there can be uncertainty about exactly how people categorise their trip purpose at this detailed level Also, the RSI data is for trips crossing the RSI cordons which are likely to be longer than average, and may not represent the spread of journey times across the entire study area For these reasons, this comparison is not considered part of the formal model validation: validation has focussed on matching the RSI cordon-crossing volumes (Figure 7-1) rather than making an effort to match these proportions The results shown in Table 7.4, are reported by journey purpose for Home Base Work (HBW), Home Based Employers Business (HBEB), Home Based Personal Business and Shopping (HBPBS), Home Based Recreation and Visiting Friends (HBRV), Non-Home Based Employers Business (HBEB) and Non-Home Based Other Trips (NHBO). Table 7.4: Time of Day Split (% of 12 hour trips) by Purpose for travel by car, RSI versus model. 5 07:00-10:00 10:00-16:00 16:00-19:00 Purpose RSI CSRM RSI CSRM RSI CSRM HBW 57% 45% 16% 20% 27% 35% HBEB 35% 32% 42% 38% 23% 30% HBPS 17% 19% 64% 61% 19% 20% HBRV 12% 16% 48% 40% 40% 43% NHBEB 23% 11% 61% 79% 16% 9% NHBO 13% 9% 58% 70% 30% 20% All trips 32% 24% 44% 46% 25% 29% 5 The All Trips data from the CSRM model given in this table is calculated using only the purposes listed individually. Since the CSRM model does not include Time of Day choice for Education it is not considered appropriate to include these purposes here. The RSI All Trips proportions have also been calculated without Education trips in order to compare like with like. 100 CSRM Model Development Report

109 !:+ 8&8#864#=530=5=35< = 38&3=0=<& This section presents comparisons between the validated SATURN Local Highway Matrices and the synthetic light vehicle trip origin-destination matrices produced by the model. The synthetic matrix used for this comparison has been converted to 1-hour vehicle trips as in Section In addition the intrazonal trips have been filtered out of both matrices as they are not assigned in the LHM and are not really a validated aspect of it, and the external-external trips have been filtered out of the LHM matrix as they are not in the synthetic matrices Table 7.5 below compares the total trip origins for the demand model against the LHM matrix 6, by internal district/external area and purpose category In the Table LHM refers to the base year SATURN Local Highway Model demand matrices; Model refers to the synthetic highway demand matrices generated by the base year model run The model data has been converted from totals by Time Period to 1-hour vehicle trips by using the factors contained in Table 6.8 in order to compare like with like. Since these factors are estimates based on RSI data, some variation between the figures would not be unexpected Since only the SATURN matrices contain external-external trips these have been suppressed in the analysis of the SATURN data In addition, it is believed that the data on intrazonal trips in both sets of matrices are incomparable due to the varying methods of their construction, and so this data has been suppressed in the analysis of both sets of data. This will create complications for comparison in that variations in the proportion of trips that are intrazonals will affect the trip distributions, which will create variation in the figures for all time periods The two matrices are similar in aggregate, though they show some variation in purpose split by district and time of day. There is significantly more variation in the external areas than there is in the internal districts. Overall the model has slightly more trips than the LHM matrices in each time period. 6 SATURN LHM Version 10, received on 9 th May, CSRM Model Development Report 101

110 Table 7.5: Comparison of Light Vehicle numbers of trip origins: LHM v10 vs CSRM synthetic matrix ANALYSIS BY TRIP ORIGINS SATURN data excludes External-to-Externals, both sets exclude Intrazonals. (SATURN V ) All units are 1 hour vehicle trips Area District Purpose LHM-AM Model-AM Diff (AM) % Diff LHM-IP Model-IP Diff (IP) % Diff LHM-PM Model-PM Diff (PM) % Diff Internal Cambridge Disc 3,180 1,886-1,294-41% 4,504 4, % 3,633 3, % EB % 1,914 1, % 1,742 1, % HBEd % % % HBW 3,301 4,557 1,256 38% 1,197 1, % 7,698 7, % Cambridge Total 8,381 7, % 7,974 7, % 13,702 13, % East Cambs Disc 1,365 1, % 2,457 2, % 2,485 2, % EB % % % HBEd 808 1, % % % HBW 4,421 4, % % 1,897 2, % East Cambs Total 7,167 7, % 3,502 3, % 4,928 5, % Huntingdonshire Disc 4,193 4, % 7,730 9,095 1,366 18% 8,134 9, % EB 1,246 1, % 1,588 1, % 1,415 1, % HBEd 2,188 2, % % % HBW 8,500 10,759 2,259 27% 1,447 1, % 7,122 7, % Huntingdonshire Total 16,127 19,285 3,158 20% 11,095 12,588 1,492 13% 17,256 18,883 1,627 9% South Cambs Disc 4,506 4, % 9,025 9, % 9,236 9, % EB 1,701 1, % 1,437 1, % 1,408 1, % HBEd 2,743 2, % % 997 1, % HBW 9,704 9, % 1,454 1, % 6,721 7, % South Cambs Total 18,654 18, % 12,424 13, % 18,362 18, % Internal Total 50,329 53,060 2,730 5% 34,995 36,825 1,829 5% 54,248 56,606 2,358 4% External London Disc % % % EB % % % HBEd HBW % % % London Total % % % Rest of E EnglandDisc 1,988 2, % 2,924 2, % 4,218 4, % EB 1,132 1, % % % HBEd % % % HBW 6,520 6, % % 2,581 4,660 2,079 81% Rest of E England Total 9,914 10, % 4,383 4, % 7,710 10,100 2,390 31% Remainder of GB Disc % % % EB % % % HBEd % % % HBW % % % Remainder of GB Total 1,006 1, % % % South East Disc % % % EB % % % HBEd % HBW % % % South East Total % % % External Total 11,511 12,607 1,096 10% 5,449 6, % 9,350 12,793 3,443 37% Grand Total 61,840 65,666 3,827 6% 40,444 42,988 2,544 6% 63,598 69,399 5,801 9% Internal Disc 13,245 12, % 23,715 25,705 1,990 8% 23,489 24,718 1,229 5% EB 4,494 3, % 5,336 4, % 4,884 4, % HBEd 6,664 7, % 1,329 1, % 2,437 2, % HBW 25,926 29,288 3,362 13% 4,614 4, % 23,438 24,815 1,376 6% Internal Total 50,329 53,060 2,730 5% 34,995 36,825 1,829 5% 54,248 56,606 2,358 4% External Disc 2,299 2, % 3,528 3, % 4,929 5, % EB 1,506 1, % 1,096 1, % 1,086 1, % HBEd % % % HBW 7,425 8, % 760 1, % 3,155 5,747 2,592 82% External Total 11,511 12,607 1,096 10% 5,449 6, % 9,350 12,793 3,443 37% Disc 15,544 15, % 27,243 29,517 2,274 8% 28,418 30,395 1,976 7% EB 6,000 5, % 6,432 6, % 5,970 5, % HBEd 6,944 7, % 1,394 1, % 2,617 2, % HBW 33,351 37,555 4,204 13% 5,374 5, % 26,593 30,561 3,969 15% Total 61,840 65,666 3,827 6% 40,444 42,988 2,544 6% 63,598 69,399 5,801 9%!:! 5&35#05= Realism tests involve changing the cost or time functions within the model and determining the resulting change in output demand. The three main tests performed were: car fuel own-cost vehicle-km elasticity, where fuel cost is increased by 10%. Nonfuel vehicle operating costs (for business trips) are not changed. car own-travel time trip elasticity, where car in-vehicle time is increased by 10%. 102 CSRM Model Development Report

111 public transport fare own-cost trip elasticity, where fares are increased by 10% including bus, rail and Park & Ride fares All these tests are performed using the 2006 model. The base case uses the validated 2006 LHM highway matrix v10 and its cost skims. Highway demand can be measured in three places given the model structure. Recalling the notation of Section 6.13, there are four highway matrices produced: B, the base validated SATURN highway matrix (produced by Atkins) X, the base TDM synthetic matrix (validated against SATURN in 2006) Y, the alternative synthetic matrix produced by the TDM (eg. with fuel costs increased 10%) F, the pivoted alternative-case LHM matrix Broadly each matrix cell in F is defined as described in Section 6.13 F odct = B odct * Y odct / X odct (odct = origin,destination,user class and time period) In the alternative cost scenario the demand model is iterated to convergence with the LHM. This ensures that changes in vehicle generalised cost due to re-routing and congestion changes are fully included in the elasticities reported Previously a pair of tests was made, one with fuel price increased by 10% and one where it was reduced by 10%. The resulting elasticities of response were quite similar, demonstrating that it is not necessary to perform both tests routinely The vehicle-km produced by matrix B are determined as the sum of the trips in B times the distance skim D(B) for each zone pair obtained by assigning B in the LHM: VKM(B) = sum(b od *D(B) od ) (for a given user class and time of day) The synthetic matrix X is never assigned, but takes the distance skims from the base LHM, so VKM(X) = sum(x od *D(B) od ) The forecast matrix F is assigned and produces distance skims D(F), so and VKM(F) = sum(f od *D(F) od ) VKM(Y) = sum(y od *D(F) od ) So the three ways that highway demand change can be measured are: change in the demand model (matrix Y versus X) change in highway matrices after pivoting, ie. the change in the matrix F versus B. change in assigned vehicle-km in the LHM, summed over all road links in the study area. These will include the part of through traffic that is assigned within the study area, but do not include intrazonal movements For public transport fares, there is no pivoting process and there is only the demand model matrix elasticity Car own-time elasticity is similarly calculated as the change in trips due to a 10% increase in travel time. This is only a single-cycle run without pivoting as the WebTAG values to which it is to be compared are from Stated Preference surveys For each test the elasticity results have been calculated using the formula recommended by WebTAG CSRM Model Development Report 103

112 e = (log (T1)-log (T0))/ (log (C1)-log (C0)) where T0 and T1 are travel demand respectively for the Base and Alternative case, and C0 and C1 are the travel cost/time variable for the Base and Alternative case. Since the cost has been increased by 10%, log(c1)-log(c0)=log(1.1) Results are reported below (Table 7.6 to Table 7.10 and Table 7.12) for the eight LHM user classes, with subtotals for HBW and for Other, so that the tables include results for the four main purpose (HBW, Education, EB and Other). For car own-time elasticity the results (Table 7.11) are reported for the TDM purposes with totals for Education, EB and Other All elasticities reported represent the typical weekday that CSRM modelled, for either the 3 and 6 hour periods of the demand model matrices, or 1 hour periods for the LHM based results Tours starting in external zones are largely an exogenous input to the model. Such trips have distribution and mode choice responses, but cannot choose destinations other than the study area, so they are not fully responsive. Also, the fuel elasticity of -0.3 is a national number i.e. it relates to a population of trips consistent with the national average; whereas in the model the trips by residents are a comparable population sample, the whole matrix of trips by residents plus inbound travellers is a sample skewed towards longer trips It is therefore more appropriate to calculate an elasticity that includes only trips by residents, plus NHB trips with origins in the study area. All the demand matrix elasticities are done this way. Unfortunately this is only possible where the productionattraction matrix is available, and for the LHM it is not possible to isolate trips by residents. Therefore the LHM matrix elasticities are for all O-D movements with origins in the study area (Table 7.8), or for internal-internal trips only (Table 7.9), and the LHM network elasticities are for links in the study area including all traffic (through traffic, residents and non-residents, etc.) At the head of the results tables this information is given in one of the forms below: VKM(B Orig in study area) is the sum of VKM [Vehicle km] produced by the matrix B over all destinations and over origins in the study area. Trips(X P in study area) is the sum of trips produced by the matrix X over all trips where the P-A matrix production is in the study area (residents, for HB trips). This is only possible for X and Y, as P-A information is not available for matrices B and F.!:- 5&35593 & # For this test, fuel cost has been increased by 10%. The tables following show respectively: the response of the synthetic demand model (Table 7.6); the response of the synthetic demand model by socio-economic class for HBW trips (Table 7.7); the LHM pivoted matrices with origins in the study area (Table 7.8); internal to internal movements in the LHM pivoted matrices (Table 7.9); and 104 CSRM Model Development Report

113 the LHM network traffic in the study area (Table 7.10) The overall fuel price elasticities are currently reasonable (synthetic matrix 12 hour elasticity of -0.27). The WebTAG guidance suggests an annual figure of It would be expected that Cambridge would have lower elasticities as it is more congested and has higher income than average The lambdas for all purposes for Car and PT are tabulated in Table 6.1. Most purposes are using lambdas at 0.75 or 0.8 times WebTAG median values for car travel, and at 1.2 times WebTAG Median values for PT travel. The model uses WebTAG Median mode choice versus distribution sensitivity parameters for all purposes. Education trips use WebTAG Median values throughout except for the PT distribution lambda which is 1.2 times the median as for all other purposes The synthetic and LHM matrix responses reported below are not entirely comparable as one refers to residents and one refers to the trip origins. In the AM peak, where these definitions will be most similar, the results are versus In the PM peak, the trips with origins in the study area will have many return legs of tours generated outside. These are expected to be less responsive than the residents, which explains the versus Taking only destinations in the study area (in the final column of Table 7.8) gives a PM elasticity of -0.20, but this is still an underestimate of the elasticity experienced by residents Another way to compare results is to consider elasticities for the whole matrix excluding intrazonals (and external-external trips from B and F). Of course this is lower than the residents elasticitity as it has many more trips that are not fully responsive. That matrix has an overall synthetic elasticity (Y/X) in AM, IP and PM periods of -0.17, , whereas in the LHM (F/B) they are (-0.16, -0.21, -0.20). These have not been tabulated below as the tables concentrate on the numbers related to validation criteria For the Other purpose, the relative behaviour of different income groups is relatively flat in the synthetic matrix but is in the direction of high income being more sensitive to cost. The reverse effect is seen in the outturn LHM elasticities. This has been checked in detail and is entirely due to the proportions and trip lengths of a small number of long trips out of the study area; elasticities for trips entirely within the study area clearly show decreasing sensitivity with income in both the synthetic and LHM matrices (the latter is shown in Table 7.9). The aggregate elasticities across all incomes remain similar between the two matrices The relative sensitivity of AM, interpeak and PM peak are very similar, but in general the interpeak is less sensitive CSRM Model Development Report 105

114 Table 7.6: Demand model synthetic matrix car own-cost elasticity (vehiclekm with respect to fuel cost), study area residents only, by user class and time period In the notation above, this is VKM(Y P in study area) / VKM(X P in study area) 7 Purpose AM 7-10 Interpeak 10-16:00 PM peak 16-19:00 12 hour HBW Low Income HBW Medium Income HBW High Income HBW - all incomes Education EB Other Low Income Other Medium Income Other High Income Other - all incomes All light vehicles Table 7.7 shows the same HBW results by GSeC 8 segmentation, which has greater variation of response between segments. The different GSeC groups have a range of elasticities from to -0.08, but when these are blended as income groups the variation is smaller. Table 7.7: Demand model synthetic matrix car own-cost elasticity (vehiclekm with respect to fuel cost), study area residents only, for HBW by socioeconomic classification In the notation above, this is VKM(Y P in study area) / VKM(X P in study area) GSeC AM 7-10 Interpeak 10-16:00 PM peak 16-19:00 12 hour Full Time GSec GSeC GSeC GSeC All Full Time Part Time GSeC GSeC GSeC GSeC All Part Time All HBW See Section for the definition of this terminology. 8 GSeC is a grouping of 2001 Census NS-SeC categories used in the model development, with GSeC 1 relating to professional occupations with higher skills/earnings, and GSeC 4 relating to lower skills/earnings. See Glossary 106 CSRM Model Development Report

115 7.8.9 The next tables relate to the LHM matrices (F and B) which are actually assigned. Note that the lower PM peak numbers in Table 7.8 arise because these consider origins in the study area, rather than residents, and consequently do not just include fully responsive trips. The final column considers destinations in the study area (SA) which is closer to a residents elasticity and shows a larger response of -0.20, but this still includes a component of non-residents trips. Table 7.8: LHM matrix elasticities (vehicle-km with respect to fuel cost) all traffic originating in the Study Area, by user class and time period based on 1-hour assignment In the notation above, this is VKM(F Orig in study area) / VKM(B Orig in study area) OR VKM(F Dest in study area) / VKM(B Dest in study area) User AM peak Interpeak PM peak PM peak Purpose Class (Orig in SA) (Orig in SA) (Orig in SA) (Dest in SA) HBW Low Income UC HBW Medium Income UC HBW High Income UC HBW - all incomes UC1-UC Education UC EB UC Other Low Income UC Other Medium Income UC Other High Income UC Other - all incomes UC6-UC All light vehicles UC1-UC Table 7.9: LHM matrix elasticities (vehicle-km with respect to fuel cost) internal to internal movements only, by user class and time period based on 1-hour assignment VKM(F Orig and Dest in study area) / VKM(B Orig and Dest in study area) User Purpose Class AM peak Interpeak PM peak HBW Low Income UC HBW Medium Income UC HBW High Income UC HBW - all incomes UC1-UC Education UC EB UC Other Low Income UC Other Medium Income UC Other High Income UC Other - all incomes UC6-UC All light vehicles UC1-UC CSRM Model Development Report 107

116 Table 7.10: Network elasticities of vehicle-km on links in study area with respect to fuel cost, by user class and time period Purpose User Class AM peak Interpeak PM peak HBW Low Income UC HBW Medium Income UC HBW High Income UC HBW - all incomes UC1-UC Education UC EB UC Other Low Income UC Other Medium Income UC Other High Income UC Other - all incomes UC6-UC All light vehicles UC1-UC The link loads whose response are measured in Table 7.10 are demand flows from assignment of peak hour matrices, i.e. without SATURN s deferral of queues to subsequent time periods. &8<#7558# The car own-time trip elasticities are shown in Table 7.11, based on a single model cycle as specified above. The number of trips made decrease with an increase in journey times for almost all the purposes, with an overall elasticity of (which is well within the WebTAG recommendation of being negative and smaller in magnitude than -2.0) HBEB has a positive elasticity in the interpeak, due to time period switching from other periods where journey times are slower, but its 12-hour elasticity is still negative The other exception is NHB trips, where CSRM has a positive elasticity (produces more trips). This is because NHB trips are produced based on HB trip attractions in the study area. The increase in car time causes HB trips to shorten, so fewer study area residents travel out of the study area. HB trips from outside the study area are not able to choose not to come to the study area, so the total HBEB trip ends in the study area increase, causing additional NHB trip generation This effect produces small numbers of trips and the net effect is almost zero (the overall 12-hour elasticity of EB is 0.00 and the 12-hour elasticity of NHBO is also 0.00). 108 CSRM Model Development Report

117 Table 7.11: Demand model synthetic matrix car own-time elasticity (Car trips with respect to an increase in journey times) In the notation above, this is Trips(Y P in study area) / Trips(X P in study area) Purpose AM 7-10 Interpeak 10-16:00 PM peak 16-19:00 12 hour HBW Education - School Education - Tertiary Education HBEB NHBEB Total EB HBPS HBRV NHBO Total Other All Purposes &#86&558# The public transport fare test results are shown in Table The PT trip elasticities are slightly lower than might be anticipated, particularly for Education. However, the overall elasticity (-0.26) is within the range recommended by WebTAG (Sec ), which is from -0.2 to -0.4 for changes taking place within 12 months or up to -0.9 over longer periods The HBW elasticities decrease with increasing income as might be expected. For discretionary/other there is a similar pattern. Table 7.12: Public transport own-cost elasticity (PT trips with respect to fares) In the notation above, this is Trips(Y P in study area) / Trips(X P in study area) Interpeak PM peak Purpose AM : :00 12 hour HBW Low Income HBW Medium Income HBW High Income HBW - all incomes Education EB Other Low Income Other Medium Income Other High Income Other - all incomes All Purposes !:' 8#398# The synthetic demand matrix for the car mode largely reproduces the trip volumes, trip lengths and trip length distributions observed by the Road Side Interviews in 2006, by purpose and time of day CSRM Model Development Report 109

118 7.9.2 The CSRM model has parameters within the range specified by the WebTAG guidance available, and has responses to fuel cost, car time and fare elasticities within the ranges required by WebTAG The pattern of detailed responses is generally sensible. The variation in responses between different income groups is not as large as initially expected. For HBW it can be seen that there is a larger variation by socio-economic group, but the spread becomes smaller when they are blended into income segments. For Other/discretionary, the variation of elasticities is initially counter-intuitive, but this is to do with the higher incomes having some very long (and higher cost) trips in the demand model, and within the study area the responses decrease with income as one would expect This chapter of the report demonstrates that the demand model has been appropriately calibrated and validated in line with current modelling and appraisal guidance. 110 CSRM Model Development Report

119 8 Public Transport and Cycle Validation -: 855< This chapter describes the data available for validation of non-car modes, and how the model compares to those observations The validation of the public transport and cycle assignment models can also be regarded as an indirect validation of the up-stream submodels of travel demand and land use activity, as it is the demand matrices produced by these up-stream models that give rise to the link loads The following sources of observed data are described in this chapter: the 2009 bus survey, commissioned by Cambridgeshire County Council (CCC) for the CSRM project; ticket sales data from Stagecoach, comparing several weeks in 2006 and several weeks in 2009, to show the year-on-year and seasonal variation in bus usage; the 2006 Roadside Interviews (RSI) collected at Park & Ride sites; the LATS 2001 rail survey; other CCC bus monitoring data; CCC cycle cordon counts In most cases the validation year for public transport (PT) and cycle is 2006 with model results for 2006 to be compared with data sets which are either for 2006 or close to The bus survey, LATS rail boarding data and the P&R boarding data was used in calibrating modal volumes to some extent, otherwise the remainder of the observed data above was not used in model calibration Verification also needs to be undertaken to ensure that the model supply and simulation adequately represent transport infrastructure and services WebTAG (Unit , para ) requires that across modelled screenlines, modelled flows should, in total, be within 15% of the observed values. On individual links in the network, modelled flows should be within 25% of the counts, except where observed flows are particularly low (less than 150) It should be borne in mind that the 2006 model results have already been constrained to observed ONS journey-to-work (2001) and National Pupil Database school travel data (2006) as part of the incremental modelling process described in section 6.8. This step introduces a further constraint in terms of the match that can be achieved between the modelled and observed counts in cases where the ONS/NPD data contradicts the counts. The incremental modelling process itself was conducted cautiously to avoid applying inappropriate constraints where the confidence in the data was low The model results represented below are taken from run series 2006 T342\TDM1. -: # The primary source for bus mode validation was a survey conducted in March and April The survey was commissioned by Cambridgeshire County Council through Atkins, and conducted by Sky High Traffic Surveys CSRM Model Development Report 111

120 8.2.2 The survey included 12-hour counts of total bus ridership at six cordon points around Cambridge. These counts were made by enumerators boarding each bus service passing the cordon point, not using any roadside counts While on the buses, the enumerators conducted face-to-face passenger surveys, including questions about trip purpose, origin and destination postcode, and boarding and alighting location Counts and surveys were made in both directions for the period 7 AM to 7 PM, which covers the CSRM demand model time periods. Figure 8-1 Bus Survey Locations (Cordon points marked with pink bars) Source: Cambridgeshire County Council Website; amended by Atkins and WSP 9 Details are available in WSP Technical Note 1237/054 and 1237/ CSRM Model Development Report

121 5=86819&5#0589# Because the TIF modelling involves road user charging within Cambridge, and a package of complementary measures especially public transport across the charging area boundary, the survey cordon is designed to measure the existing public transport demand across such a boundary. However, as much of the bus activity in Cambridge takes place within the urban area, the cordon points are generally located within the proposed road user charging area, rather than at the charging boundary itself. This decision was taken in order to capture something closer to peak loads on bus services. The locations have been selected as follows (see also pink bars on Figure 8-1 above): Milton Road: Inside Cambridge Science Park, to capture commuting by bus from urban area to/from that important trip attractor. Cordon coincides with point Guided Bus will join Milton Road. Newmarket Road and Hills Road: Railway line used as cordon. Note that the Hills Road bridge was undergoing long-term roadworks at the time. The impact of this on bus patronage is not known, although the use of stagecoach ticket sales data described later in this chapter helps control for total patronage. Madingley: Cordon located to capture bus use between town centre and West Cambridge University sites. Huntingdon Road/Girton Road and Trumpington Road: Cordon located further out to capture inter-town (Huntingdon-Cambridge, Royston-Cambridge, Trumpington- Cambridge) bus use prior to the introduction of the Guided Bus. The C6 service was surveyed on Girton Road just before it enters Huntingdon Road, but this is treated as part of the same Huntindon Road site (the same pink bar on the map) While a number of services pass through both the Trumpington Road and the Hills Road cordons, each such service surveyed was surveyed at only one of these sites, thus ensuring that there was no double-counting A pilot survey on Madingley Road was run on 12 March 2009 during school and university term, while the whole cordon was surveyed during the week of 30 March- 3 April, This included re-surveying the Madingley Road site. (As the format of the survey was reasonably manpower-intensive, it was not possible to make all the counts on the same day.) The latter week was in school term, but not in university term. The counts from the pilot have been preferred for the Madingley Road site because of the University term issue, Dedicated Park & Ride bus services were deliberately omitted from the survey, as they were surveyed in the 2006 Roadside Interview process. Private buses (eg. dedicated school buses or shuttles to specific employment sites) were not counted Although the sites do not form a watertight cordon (they omit Histon Road consequently not monitoring the Citi 7 route which serves three villages to the north of the A14 and Coldham s Lane), and they do not count some very infrequent services, they do cover the majority of public bus services into and out of Cambridge. The model has been checked and the flows on these unmonitored routes are relatively small and broadly consistent with the number and frequency of services on those routes CSRM Model Development Report 113

122 Therefore the choice of survey locations should be sufficient for understanding traveller behaviour and checking model volumes for validation purposes. 55< Atkins carried out a review of the survey results against Census Journey to Work data 10 and concluded that: The bus passenger survey was able to capture a good sample for the bus passenger trip originated from within Inner Ring Road. However, for the movements originated from rest of Cambridge and Outside Cambridge, the bus passenger survey only captures about 20% of all journey to work trips. Whilst the Citi bus services (which provide regular bus services for short and medium distance trips for the Cambridge area) have a good sampling rate, the long distance bus services, such as from Huntingdon/Newmarket/St Neots, have generally poor samples and many of the long distance services were not surveyed. #8#718&& During the survey, some buses could not be boarded, either because they were full, or the driver was unaware of the survey. Adjustments have been made to the survey results to correct for this Using information supplied by the survey company about exactly when and where this non-boarding occurred, an adjustment has been made as follows. Full buses have been estimated as 60 passengers, while all other not-boarded buses have been assumed to have a count equal to the average for that service, location and time period that were sampled. In total this adds about 3% to the estimated cordon counts, as shown in Table & This section presents some summary totals by site and service to provide an overview of the count and survey results. The survey in particular contains a wealth of detail, but which is not directly relevant to this validation and is beyond the scope of this report. More detailed counts by time of day, adjusted to 2006 estimates, are presented in subsequent sections for comparison with model results. Additional tables can also be found in Appendix F Table 8.1 shows the number of passengers counted at each site in the pilot and main survey week. It also shows the adjustment for buses that could not be boarded, and the adjustment for year and season to produce a 2006 term time estimate. The scale factors for adjusting from 2009 survey time to 2006 in university term are derived in Section 8.3: they are 87%=1/1.15 for the main survey, or 83%=1/1.2 for the pilot. These factors are not site specific. It can be seen that the totals for Madingley Road are not that different after this adjustment (1,985 for the pilot versus 1,915 for the main week), but as this site is more affected by the university term than average, the pilot study result was preferred. 10 (Atkins, Technical Note Bus Passenger OD Data Expansion Factor Methodology Note, June 2009) 114 CSRM Model Development Report

123 Table 8.1: Survey results by site, and adjustments for missing buses and for year and season Passenger count from survey Adjusted for nonboardable buses Scale to 2006 termtime 2006 estimate for validation Survey Location/Road Madingley Road PILOT 2,304 2,382 (+3%) 83% 1,985 Madingley Road 2,117 2,202 (+4%) 87% 1,915 Huntingdon Road 2,523 2,703 (+7%) 87% 2,350 Girton Road (+0%) 87% 490 Milton Road 1,322 1,344 (+2%) 87% 1,169 Newmarket Road 2,004 2,085 (+4%) 87% 1,813 Hills Road 5,892 5,992 (+2%) 87% 5,210 Trumpington Road 1,690 1,750 (+4%) 87% 1,522 Total (using pilot for Madingley Road) 16,299 16,819 (+3%) 14, Table 8.2 shows the numbers of passengers who answered the survey questionnaire by site and service compared with the passenger count from the survey As the surveyors remained on the buses for some time, the passengers surveyed are not necessarily exactly the passengers that were on the bus at the time of crossing the count point. However, this is not believed to have had any noticeable impact on the results. The percentage shown (ratio of the two) is nevertheless a useful indication of the coverage of the survey. Also, it has been assumed that the purpose split of the passengers who were surveyed is representative of the passengers at the cordon crossing point CSRM Model Development Report 115

124 Table 8.2: Comparison of numbers of passengers surveyed with passengers counts, by site and service Survey Location/Road Madingley Road PILOT Madingley Road Huntingdon Road and Girton Road Milton Road Newmarket Road Hills Road Trumpington Road Bus Service Number of Passengers Surveyed Passenger Count at cordon Survey as % of passengers at cordon CITI 4 and UNI ,666 10% X % Total 158 2,304 7% CITI 4 and UNI % X % Total 472 2,117 22% C % C % % ,002 16% 15 and 15A % Total % C % 9 and X % Total 665 1,322 50% C % 10 and 10A % 11 and % Total 400 2,004 20% C ,463 18% C ,897 18% CITI 4 and UNI % 1A % 13, 13A and X % Total 1,221 5,892 21% C ,442 28% % Total 502 1,690 30% Main survey excluding pilot 3,980 16,112 25% &198#86& Passengers were asked to classify their activity at both their origin and their destination from the multiple-choice list in Table 8.3. They could also provide additional information, which helps to clarify some of the Other responses From these activity responses, the trip has been assigned to one of the 7 purposes in the CSRM Transport Demand Model (see Table 2.3). Many trips are straightforward: if one end is Home and the other end is Main Place of Work then the trip is Home Based Work. Non Home Based trips are generally classified based on the destination purpose. Several other assumptions have been made in order to make most effective use of the data. 116 CSRM Model Development Report

125 Table 8.3: Frequency of survey responses regarding activity at origin and at destination Activity at Origin Activity at Destination Response Responses % of samples Responses % of samples Home % % Main Place of Work % % Attending School or % % College Employer s Business % % Leisure/ Visiting % % Shopping or Personal % % Business Other % % Did not answer % % TOTAL % % 8#65#5#5&3#,,' Ideally, it would be desirable to have clear confidence intervals for all bus survey results presented. In practice this is not possible, especially given the single day survey. Table 8.4 summarises the potential sources of error in bus survey results, and the steps that have been taken to compensate for them With a single-day count it is not possible to estimate the variance of the day-today variation due to weather, random variation, etc. The Stagecoach ticket sales data was only available as totals for whole weeks and at times of year when there are seasonal changes such as students returning to university, so it is difficult to estimate a variance from that. WSP would suggest allowing at least 15% range for cordon totals and 25% for individual sites/corridors, broadly in line with WebTAG validation criteria Variation by service on the same corridor will be even larger than this, since passengers will often board the first suitable service that arrives. Service loadings are also more affected than corridor loadings by changes in timetables or route network between 2006 and Therefore validation is not presented by service at all, and the split of passengers between similar services in the model should not be considered a validated output of the model CSRM Model Development Report 117

126 Table 8.4: Potential sources of error or variation in bus passenger counts Issue Day-to-day variation in bus ridership Counts in 2009, model in 2006 Main survey not in university term Some buses could not be boarded Some lower frequency services that are in the model but were not included in survey design. Estimated magnitude ±15% in cordon totals, ±25% in individual sites (corridors) +20% -5% Compensating action Allow an error range in validation (See comment above) Counts scaled for year and season based on bus ticket sales, as described in the next section. Note that this adds error and uncertainty, particularly to individual sites as scaling was not site specific. -3% Adjustments by site as described above -2% Would expect model counts to be slightly higher than observed. Number of buses in AM peak will be small Any conclusions based on the passenger survey (notably the estimated trip purpose split) will have larger errors again, because of sampling or other variations in the survey (eg. willingness to answer the trip purpose questions). -:" 1&;7&#0&#5&8#&3858# Because the survey was conducted in 2009, and mostly out of university term time, it is not directly comparable to the 2006 model. This is because there was significant growth in bus patronage in Cambridge between 2006 and 2009, and because the model is defined to be for a typical weekday in term, not an average day Ticket sales data was received from Stagecoach Cambridge for specific weeks in 2006 and 2009, as described below. They provided data for those bus routes surveyed which are operated by Stagecoach, which includes the great majority of routes and passengers across the cordon. This was used to obtain a reasonably accurate estimate of the overall levels of bus use in the different weeks in 2006 and The approach adopted was to determine an overall seasonal adjustment scaling factor to convert the 2009 count into an estimate of the 2006 term time passenger volume. The remainder of this section explains the reasons why such a broad-brush approach was adopted and derivation of this factor The ticket sales data was only available for whole weeks including weekends and for whole routes without any segmentation by location or direction of service when the sale occurred. Therefore the average level of ticket sales cannot be used to crosscheck the cordon counts in any meaningful way. (Different services will have quite different proportions of intra-city versus cross-cordon traffic, especially as some cordon points were much further out than others. Similarly the proportion of weekday versus weekend traffic and other factors will vary.) Also the number of passengers is actually an estimate by Stagecoach from the sales data passenger boardings on the popular daily and weekly tickets are estimated using a fixed multiplier of ticket sales, rather than from the driver s record of such boardings, as the latter is believed to be less accurate. 118 CSRM Model Development Report

127 8.3.5 The sales data therefore cannot be used to look at specific times of day or dayto-day variation. It would be difficult to analyse for specific corridors, because many services run through the city centre through different count sites. There have also been some changes in network and service frequency between 2006 and 2009 (eg. the introduction of the Citi 4 service to from Cambourne to King s Hedges) so some services grow more than others do However, analysis of the ticket sales data shows that the relative levels of bus use in the different weeks are similar for a wide range of services. Even where new services have been introduced, some of the passengers will have transferred from other routes and therefore the total will be more representative than the growth on individual services It was therefore decided to use the ratios between total estimated passenger boardings across all routes to determine the seasonal adjustment factor Figure 8-2 shows ticket sales as an index where the week beginning 8/10/2006 is defined to be an index of 1.0. This week occurred within the time period during which the RSI Survey was being carried out. By taking this as the target level for model validation we are ensuring that the Bus figures we are using are comparable with the RSI Car data The totals in Figure 8-2 exclude the Citi 5 service, as a different set of weeks in 2009 was provided for that service (February term rather than March). Figure 8-3 shows the Citi 5 totals, by way of illustrating that the level of week-by-week variation in a single service ridership is larger than the variation in the total, but it does show quite similar variation between 2006 and (Since the Citi 5 service has not changed route between 2006 and 2009 the numbers should be comparable.) Week we 24/9/2006 (S) we 1/10/2006 (S+U) we 15/10/2006 (S+U) we 8/10/2006 (S+U) we 22/10/2006 (S+U) we 5/11/2006 (S+U) we 15/3/2009 (S+U) we 22/3/2009 (S) we 29/3/2009 (S) *SURVEY we 5/4/2009 (hol) Sales index (8th Oct 2006=1) (S) = School term, (S+U) = School and Cambridge University term (hol) = school holiday index of 1.0 = Oct 2006 Figure 8-2: Stagecoach ticket sales index for selected weeks, for routes into and out of Cambridge (excluding Citi 5) Source: Stagecoach Group plc, used by permission CSRM Model Development Report 119

128 we 24/9/2006 (S) we 1/10/2006 (S+U) we 8/10/2006 (S+U) we 15/10/2006 (S+U) Week we 22/10/2006 (S+U) we 5/11/2006 (S+U) we 15/2/2009 (S+U) we 22/2/2009 (S+U) we 1/3/2009 (S+U) Sales (8th Oct 2006=1) (S) = School term, (S+U) = School and Cambridge University term (hol) = school holiday index of 1.0 = Oct 2006 Figure 8-3: Stagecoach ticket sales index for Citi 5 service for selected weeks Source: Stagecoach Group plc, used by permission In summary (see Figure 8-2 and Figure 8-3), there is an increase of about 20% (index 1.2) in overall ridership from 2006 to 2009 in comparable weeks in term. However, the main survey week out of University term has an index of 1.15, so to obtain validation totals from the survey we divide by 1.15 rather than 1.2. In effect, the 5% drop from university holidays partly offsets the 20% growth For the Madingley Road site, we use the counts from the pilot day but with an adjustment of CSRM Model Development Report

129 -:( 853&395688&8# The model totals are extracted from links matching the observed cordon, so they are like with like, not including passengers on roads which were not counted. 586&456#8# The model defines time periods by time of departure whereas the survey records the time the bus crossed the cordon. This inherently creates a small mismatch, which will tend towards the model having slightly more passengers in the AM peak than the survey as more passengers will be on buses at 10:00 than at 7:00. In both cases the passengers may have a time of departure in one time period and yet be surveyed in the next hence the survey will place them in a different time period to the model. Similarly there may be slightly less in the model in the PM peak, and the interpeak should be little affected. However, as the model periods are 3 or 6 hours, this should represent only a small difference In addition, buses running late will tend to push observed passengers later in the day than they would be in the model, but this effect is also expected to be small. &#8556#8# A number of the passengers surveyed would not be modelled as having main mode bus under the CSRM modal hierarchy (see Section 2.5), either because they were making a Park and Ride trip or travelling to or from a Rail station to catch a train. The numbers of such individuals are tabulated in Table 8.5 below by survey site and time period. Rail passengers in the model can still use bus as a feeder mode and are counted in the bus passenger cordon counts reported in subsequent sections Park & Ride passengers in the model are not counted in the model totals in the next Section (8.5), in order to avoid including the dedicated P&R buses in the model in the cordon counts. It can be seen in the table that only a few passengers (56) surveyed on non-p&r bus routes were using Park & Ride, essentially all of whom were on the CITI 4 or UNI 4 at Madingley Road and Hills Road. (The Hills Road numbers for the UNI 4 are therefore counting passengers who would already have been counted at Madingley Road). Given the small absolute numbers, and the associated uncertainty in this percentage figures, no adjustment to the observed counts was felt to be necessary to compensate for this. Table 8.5: Number of Passengers surveyed who were either Park and Ride passengers or who were travelling to/from a Rail Station CSRM Main Mode Survey location Individuals Park & Ride Rail (bus passengers to/from Cambridge station within rail trip) Total surveyed % Madingley Road % Hills Road 30 1, % Others 0 2, % Total 56 3, % Trumpington Road % Newmarket Road % Hills Road 61 1, % Others 63 2, % Total 149 3, % CSRM Model Development Report 121

130 = When the Transport Demand Model was first specified there was insufficient information and budget to code dedicated school bus services into the model. The mode split of school pupils is based on the National Pupil Database, which does include dedicated services, so at the demand level the mode split is realistic. When these are assigned in the model, the pupils use public bus services This is a consequence of the design of the model, that is, the education trips are assigned but the split between public and dedicated bus services is not currently modelled For movements into schools in Cambridge this may provide a reasonable level of journey options for many pupils. It does raise a number of issues for validation: The assigned loads by service include school pupils, which should not be taken to imply that they are on those services in reality given that there are some dedicated bus services into Cambridge. In some more rural zones, public bus service levels are poor. The education mode specific constants in the model encourage parents to drive their children to local rail stations in order to access Cambridge schools. Rail is an important mode choice for some indepent schools in Cambridge, but is not a mode choice that is observed for public schools in the NPD. It may be that the model over-estimates rail use by school pupils as a result. Blocking off these rail modes without providing school buses causes unnecessarily high car use for education in those zones, so it has been left as compromise solution in the current version of the model. The bus ridership survey (main survey) identified education trips and students but did not distinguish tertiary from school students In order to compare modelled and observed counts, the numbers are subdivided in two ways: The bus surveyors counted under-16s separately obviously, this is the surveyor s estimate in the short space of time available to make such a count. Not all under-16s on public buses are making home-based education trips, but it would be expected to be a dominant trip purpose. The proportion of under-16s therefore gives an indication of the approximate magnitude of school trips. The model assignment was run with a separate trip matrix excluding home-based education trips by 5-18 year olds. This gives an estimate of the part of the model count that might not be really on public buses. -:* 853&3&8#&0&#19954&& The following charts compare the results of the latest model run with the adjusted data obtained by the survey. As described in the previous section, the model design assigns pupils to public bus services who would be on dedicated school buses in reality. To compensate for this difficulty, Figure 8-5 and Figure 8-6 present the model figures both with school education trips and without, and in the latter observed data with under-16 passengers is shown separately, with the understanding that the figures that should be compared with the survey data fall somewhere between the two values While some of the trips made by Under 16s will be for Discretionary (Other) purposes, most of them will be School Education trips. 122 CSRM Model Development Report

131 8.5.3 Figure 8-4 shows the comparison between the survey figures (adjusted as described above to give a 2006 comparison from 2009 survey data) and the model output by purpose of trip. The survey data has had 15% error bars added to it in accordance with the WebTAG validation criteria listed above. The survey data also has a number of trips of Unknown purpose where the respondent did not answer the purpose questions in enough detail. Estimated purpose Source Total Unknown Other Education EB HBW Observed Model Observed Model Observed Model Observed Model Observed Model Observed Model 15% error bars 0 5,000 10,000 15,000 20,000 25,000 Bus passengers by Purpose Observed Model (Excluding School Education trips) Model (School Education trips) Figure 8-4: Cordon Counts: Model vs Observed by Purpose (All Day) The total number of trips made is reasonable if one takes the view that most of the school age Education trips would not have been counted in the survey as they take place by School Bus. Likewise this assumption gives us acceptable levels of Education trips. The modelled number of Other trips is within the 15% error bounds, and the modelled number of EB trips is very close to the survey figure The amount of HBW trips the model predicts is visibly high relative to the survey values. Even allowing that some of the unknown trips may be HBW, this is still 20% above the survey values. A possible explanation is the under-sampling of longer HBW trips evidenced by Atkins in their review of the survey results (see ) CSRM Model Development Report 123

132 Time of Day Direction Source 12 hour PM IP AM 7-10 Inbound Outbound Inbound Outbound Inbound Outbound Inbound Outbound Observed Model Observed Model Observed Model Observed Model Observed Model Observed Model Observed Model Observed Model 0 2,000 4,000 6,000 8,000 10,000 12,000 Bus passengers in period Observed (Over 16s) Observed (Under 16s) Model (Excluding School Education trips) Model (School Education trips) Figure 8-5: Cordon Counts: Model vs Observed by TOD and Direction Figure 8-5 shows the survey and model data by Time of Day and direction. As with the previous graph the overall totals in each direction look reasonable. The interpeak is slightly low in both directions, while the AM and PM Peaks are slightly high in both directions, possibly because of the number of HBW trips relative to Other trips. 124 CSRM Model Development Report

133 Site Source Huntingdon Road/Girton Road Madingley Road Trumpington Road Newmarket Road Hills Road Milton Road Observed Model Observed Model Observed Model Observed Model Observed Model Observed Model Bus Passengers by Site Observed (Over 16s) Observed (Under 16s) Model (Excluding School Education trips) Model (School Education trips) Figure 8-6: Cordon Counts: Model vs Observed by Site Figure 8-6 shows the survey and model counts by monitoring site. As noted at the beginning of the chapter, WebTAG guidance states that the model counts at these sites should be within 25% of the survey data. In general this is achieved (if we assume that some of the school trips modelled would have been caught by the survey, and some would not have been), though the Madingley Road figures from the model are 28% over the survey data even without school trips included. -:+ 913&#8#5< 8;56&8# 5&135A89# Timetables used for the bus service coding (CCC, bus services, 2006) include bus times specified only between specific timing points, which are less frequent than bus stops In the absence of any bus journey time surveys or information from the operator, bus times between bus stops in the base year are calculated by controlling the whole route time to the timetabled duration. Hence where there is a bus stop, the time taken through the network to the next interchange point is calculated based on the congested link speeds, output from SATURN LHM assignment, and then weighted to the overall timetable time Rail services are coded in the model in terms of their headway and service patterns. The journey times between stations are derived from the timetables of the operating companies that serve the study region (First Capital Connect, One, National Express, May-December 2006). Since the model does not include crowding or delays to rail services, the service patterns are based on the timetable times CSRM Model Development Report 125

134 -:! &105=589#489#3198#8# The CCC annual traffic monitoring report for Cambridge City (CCC, 2007, Traffic Monitoring Report) makes an attempt to measure the numbers of passengers using buses on two screenlines in the city: the River Cam with monitoring points on all bridges in the city centre, counted in the Spring each year, and the radial cordon, monitoring points, bounded on the north by the A14 and the west by the M11, and roughly approximating to the City boundary, counted in the August each year However, bus passenger numbers crossing the radial are estimates determined from a mixture of ticketing information and roadside counts. Consequently they have not been used for validation purposes, and should be treated as indicative only. The only information available is for average 12 hour flow for people crossing the River Cam in March. The report shows that there are just over 147,700 journeys across the River Cam bridges in 12 hours, with 15% (approx. 22,300) of these made by bus. The report does not give the profile throughout the day These counts of buses and passengers are understood to include all passenger service vehicles including private buses such as school buses. This is one reason why these counts are larger than those obtained from the surveyed buses plus Park & Ride buses The CCC annual traffic monitoring report for Cambridge City (CCC, 2007, Traffic Monitoring Report) provides information for average 12 hour flow for people crossing the River Cam in both directions (outbound and inbound to city centre) in March 2006 and the Outer Cordon in both directions in October Table 8.6 shows that comparison between the model and observed data for the two screenlines. Table 8.6 Passengers crossing bus screenlines: Model 2006 versus CCC Traffic Monitoring Report (March and October 2006) 12 hour passengers crossing cordons in both directions (7 AM to 7 PM) Direction Model Observed 2006 Percentage difference River Cam screenline 16,838 22,925-27% Outer Cordon 17,222 26,610-35% Note: Park and Ride passengers are included in total passenger figures As these observed volumes are based on road-side estimates of bus passengers, they are not suitable for a WebTAG PT model validation, and are presented as indicative only However, it is reassuring that the model totals are lower than the supplied data, whereas earlier in this chapter the model was shown to be high relative to the latest bus survey data. This is reassuring because: the CCC data includes all buses, including school buses, private buses, and tourist or charter services, and so will include route options and bus demand not modelled within CSRM. This is not expected to be a large proportion of the total, but it would be expected to produce a larger count than the model 126 CSRM Model Development Report

135 the bus survey omits school buses some lower frequency buses and coaches that are in CSRM, so if would be expected to be slightly lower on those grounds. -:- &;&# Park and Ride data has been obtained from 2006 Roadside Interview data (RSI), supplied by Atkins, and gives the absolute number of persons boarding P&R buses and numbers of vehicles arriving at most Park and Ride sites by time of day. The data was not available for the Babraham Park and Ride site, where no survey was conducted. RSI weekday records that relate to four Cambridge Park and Ride sites were received with records relating to inbound interception of park and ride trips except at Cowley road, where the RSI intercepts outbound vehicles. This data set can be used to analyse the actual number of trips through each of the four park and ride sites by time period Table 8.7 shows the difference between the model and observed volumes of person trips and number of vehicles entering/exiting each park and ride site in each of the three modelled time periods This data has been used in calibration to enable the model output to better match the volume and site choice CSRM Model Development Report 127

136 Table 8.7: Model 2006 versus Park and Ride RSI data: number of person trips and vehicles entering/exiting each Park and Ride site by TOD 11 Site Name Cowley Road Madingley Road Newmarket Road Trumpington Road Overall Site Entries** Observed Data Cars out Passengers out Cars in Passengers in Cars in Passengers in Cars in Passengers in Cars in Passengers in TOD 2006 RSI Observed 2006 CSRM Model % Difference: Model- Observed AM* 104* 10* -90%* IP % PM % Total % AM* 173* 12* -93%* IP % PM % Total % AM % IP % PM % Total % AM % IP % PM % Total % AM % IP % PM No Data 26 Total % AM % IP % PM No Data 33 Total % AM % IP % PM % Total % AM % IP % PM % Total % AM % IP % PM % Total % AM % IP % PM % Total % *It is possible that the number of cars leaving Cowley Road in the AM peak partly represent cars dropping off passengers for the P&R services, rather than the departure of passengers who parked earlier in the day. As such, the model would not be expected to match this figure. **In the AM peak and interpeak the Observed data and Model Outputs are for all sites where the RSI measured cars/passengers entering the site. In the PM peak and in the overall Total the Model output in the PM peak for Newmarket Road has been excluded as we do not have Observed data to compare it against RSI data was not available for the Babraham Road site, so the model figures here cannot be compared in the same way. The model output data for this site is presented in Table The totals for most of the sites are the sums of the figures for the AM Peak, interpeak and PM Peak. For Newmarket Road there is no data for the PM Peak, so the totals presented are the sums of the figures for the AM Peak and Interpeak ONLY. 128 CSRM Model Development Report

137 Table 8.8: Model 2006 data output for Babraham Road Park and Ride site Site Name Babraham Road In Babraham Road Out TOD Number of Person Trips Modelled Number of Vehicles Modelled AM IP PM Total AM IP PM Total An analysis of the matching between observed and modelled P&R volume shows that overall P&R usage is under-estimated by the model. As the P&R volume is a small fraction of the total car volume, it is difficult to achieve an exact match while doing any other calibration. The demand model extracts Park and Ride usage as a subset of car usage. The number of car trips into Cambridge has recently been reduced in order to better match the RSI data shown in Figure 7-1. Since the proportion of Park and Ride usage amongst those who travel by car has not increased significantly, this has also caused a proportional reduction in Park and Ride passengers. This necessary adjustment in car volumes has had the side-effect of reducing the modelled usage of the Park and Ride sites to a level that is 20-40% below the supplied data. -:' 38#8#&5&& The LATS survey and count data for passengers entering the rail stations within the study area was provided by DfT, Rail Statistics, July The LATS 2001 survey was conducted between 2000 and Based on the description of the counts as having been undertaken at station entry points, it has been assumed that the count data includes only passengers entering the station and not passengers interchanging trains. Though both count and survey data was available, model results have been verified only against the count data as providing the most accurate representation of passenger numbers. -:, &35&#&&8# The count data received counts the number of boardings (excluding interchange boardings) at the four major rail stations (Cambridge, Ely, Huntingdon and St Neots) throughout the day. At selected other stations counts are available in the AM Peak only. For calibration purposes the interpeak and PM peak boardings at these stations have been interpolated using the average boarding proportions for the three time periods at Ely, Huntingdon and St Neots (Cambridge shows a very different boarding pattern) CSRM Model Development Report 129

138 For calibration and validation of 2006 model outputs it was necessary to scale the counts from 2001 to For this we used data supplied on the ORR website which calculates boardings for the whole year based on supplied ticket sale data for the years and We have assumed that the ratios between the boardings at the relevant stations in this data are the ratios to apply to our 2001 boardings data to produce 2006 data for comparison with the model output. (Unfortunately the ORR does not publish data for the year , and so as a consequence we have been forced to calculate and use the ratios for a five year period that is offset by one year.) This adjusted data has been compared with the model output both for the AM peak (Table 8.9) and for the whole of the modelled 12 hour period (Table 8.10). The variation between the supplied data (adjusted to 2006) and the model output has been listed both by station and as an overall figure In all tables the growth factor is the calculated percentage growth that is used to convert the 2001 counts into 2006 equivalents generated from the comparison of the and data as described above. The growth factor for the Main Stations and Total Internal rows are calculated from the totals of the 2001 count and 2006 equivalent data for the individual stations. They are listed for information only. 130 CSRM Model Development Report

139 Table 8.9: Comparison between LATS Counts and 2006 Model Output: AM Peak only Station 2001 LATS Count - AM Growth Factor Estimated 2006 Count - AM Model Output - AM Absolute Diff (Model-2006 Estimate) - AM % Diff (Model Estimate) - AM Cambridge % % Ely % % Huntingdon % % St Neots % % Main Stations % % Foxton 42 33% % Meldreth 86 53% % Shelford % % Shepreth 46 85% % Waterbeach % % Whittlesford % % Total Internal % % Table 8.10: Comparison between LATS Count and 2006 Model Output: 12 hours Station 2001 LATS Count - 12 hr Growth Factor Estimated 2006 Count - 12 hr Model Output - 12 hr Absolute Diff (Model-2006 Estimate) - 12 hr % Diff (Model Estimate) - 12 hr Cambridge % % Ely % % Huntingdon % % St Neots % % Main Stations % % Foxton 62 33% % Meldreth % % Shelford % % Shepreth 68 85% % Waterbeach % % Whittlesford % % Total Internal % % Station It can be seen that the match for the main stations overall is very good, though this clearly hides under-estimation for Cambridge and over-estimation for Huntingdon and St Neots. The situation is better in the AM peak than for the whole day, which is believed to be due to undertainty regarding the numbers of discretionary and employer s business trips in the IP and PM For the major stations we also show the interpeak and PM Peak comparison of the 2006 model output with the supplied data (adjusted to 2006). Table 8.11: Comparison between LATS Count and 2006 Model Output: Interpeak 2001 LATS Count - IP Growth Factor Estimated 2006 Count - IP Model Output - IP Absolute Diff (Model-2006 Estimate) - IP % Diff (Model Estimate) - IP Cambridge % % Ely % % Huntingdon % % St Neots % % Main Stations % % CSRM Model Development Report 131

140 Station Table 8.12: Comparison between LATS Count and 2006 Model Output: PM Peak 2001 LATS Count - PM Growth Factor Estimated 2006 Count - PM Model Output - PM Absolute Diff (Model-2006 Estimate) - PM % Diff (Model Estimate) - PM Cambridge % % Ely % % Huntingdon % % St Neots 64 37% % Main Stations % % The tables above show a similar pattern to the AM peak and 12 hour table above. Overall the figures look better than the comparisons of the individual stations. Cambridge shows a consistent under-estimation of boarding numbers and Huntingdon and St Neots a consistent over-estimation The map below in Figure 8-7 shows the Rail network within the Study Area with all the Stations surveyed by LATS labelled. Crown copyright. All rights reserved. Figure 8-7: Map of Study Area Rail Network 132 CSRM Model Development Report

141 -: 4355&#88#>55#3# Two validation cordons for cycle trips have been used: An inner cordon in the inbound direction only, where counts are limited to the AM peak time period (Figure 8-8). An outer cordon comprising inbound and outbound journeys with counts available for all three time periods (Figure 8-9) The River Cam screenline (part of the inner cordon) was surveyed by CCC in March 2006 after the end of the university term but before the schools finished. The outer city cordon was surveyed in Autumn 2006 before the university/school term. Since students are included in the model, the modelled figures are expected to be significantly higher than those surveyed. The model output suggests: 52% of cycle trips with ends in Cambridge in the AM peak are for school or tertiary education, with 42% in the interpeak and 23% in the PM peak. over the entire Study Area, roughly half of education cycle trips are made by Tertiary students in every time period, though this is likely to be an underestimate for trips within/into/out of Cambridge With all the attendant dangers of applying model output to justify related model output, this suggests that in the AM peak the model should show about a third more cycle trips across the Inner Cordon than the survey (the Inner Cordon survey will not include any cycle trips by Tertiary Students). In the interpeak and PM peak the model should exceed the survey cordon counts by about 25% and 15% respectively Across the Outer Cordon the survey will not include cycle trips by any student (school pupil or tertiary), and as a consequence the proportion of trips that the survey will be expected to miss is higher. It is expected that the model should exceed the survey cordon counts by about double, about two thirds and about a third in the AM peak, interpeak and PM peak respectively. Table 8.13 Model 2006 versus CCC data: number of cyclists crossing the Outer and Inner Cordon. Cordon TOD Model Observed Percentage difference: model vs. observed AM (7-10) 2,705 1,658 63% Outer Inbound IP (10-16) 2, % PM (16-19) % AM (7-10) % Outer Outbound IP (10-16) 3, % PM (16-19) 1,909 1,303 46% AM (7-10) 4,971 3,827 30% Inner Inbound IP (10-16) 5,470 Not available PM (16-19) 2,079 Not available Note: The cordons are not complete. Source: Cambridgeshire County Council CSRM Model Development Report 133

142 Figure 8-8 and Figure 8-9 below show the monitoring point locations of the Inner and Outer Cordon used for the model validation of cycling volumes. It should be noted that there are significant gaps in the Inner Cordon, with a number of key cycle routes into the city centre not monitored. For example neither Garret Hostel Lane, nor the towpath along the Cam to the north of Cambridge were monitored by the survey, the former in particular being reasonably well used The model output, on the other hand, does measure all cyclists crossing the cordons. It is not meaningful to form a like with like cordon extracting links in the model to match the observed, since the result would depend strongly on cycle route choice in the model. In reality, cycle route choice depends on a number of factors that cannot be captured in a strategic scale demand model For these reasons, the comparison figures presented here are thought to represent a suitable level of matching between CSRM and the observed data which is available to us. Crown copyright. All rights reserved. Source: Cambridgeshire County Council Figure 8-8 Inner Cordon cycle monitoring point locations (2007) 134 CSRM Model Development Report

143 Source: WSP plot of CCC information Crown copyright. All rights reserved. Figure 8-9 Outer Cordon monitoring point locations (2007) -: 9&486&#435&3&8# In this chapter we have presented model output against supplied data on bus patronage, park and ride usage, rail station usage and cycling in Cambridge The comparison between the model output and the bus survey data must be made taking into consideration the complications involved in reducing the 2009 passenger numbers given to 2006 levels for validation purposes Taking this into account the model output, though somewhat dissimilar to the survey data in a small number of places, reasonably approximates what the survey perceives to have been the reality in Atkins own analysis of the survey results (Atkins, June 2009) notes possible undersampling of longer distance HBW trips, which may provide an explanation for the apparent over-estimate of this purpose in the model The bus screenline data supplied by the County Council has also been presented. Because of the methodology used for the CCC screenline counts, these figures are primarily estimates, and so have not been actively used in the validation. However, given that this screenline captures services not included in the model, it is reassuring to note that the modelled figures are below these (lying between the screenline totals and the PT survey totals) CSRM Model Development Report 135

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