PLANNING FOR FIRST &LAST MILE CONNECTIVITY FOR MASS TRANSIT USERS

Transcription

1 PLANNING FOR FIRST &LAST MILE CONNECTIVITY FOR MASS TRANSIT USERS Case Study- Delhi MRTS By Anannya Das Transport Planning Department ( ) School of Planning & Architecture

2 Structure of Presentation Need & Importance of LMC Public Transportation Scenario- Delhi MRTS Delhi & Station Typology LMC Trip Characteristics Study Case Station Study Scenario Development & Evaluation Conclusion & Recommendation Approach to LMC

3 OBJECTIVE & METHODOLOGY STUDY OBJECTIVE To assess the importance of first & last mile connectivity for an efficient urban MRTS To review global best practices in planning for Last mile connectivity to urban mass transit systems To assess the existing last mile connectivity environment, trip pattern & attitudinal behaviour of Mass transit Users To Develop Last Mile access choice model To evolve alternative strategies for enhancing last mile connectivity for metro user at case station To recommend Approach & guidelines for last mile connectivity METHODOLOGY Stage I Assessing the Study Stage II Assessing the Study Area Stage III Quantitative evaluation of Trip Characteristics Stage IV Development of Mathematical model Stage V Providing Solutions

4 NEED & IMPORTANCE OF LMC LAST MILE CONNECTIVITY COVERAGE w.r.t DELHI METRO STATIONS NUTP, 2006 Focus on MASS TRANSIT Avg. Catchment Recommended service Range There are areas beyond walkable distance from MRTS station Thus to access from those area, people depend on auxiliary or feeder modes If there are no provision of sustainable modes within this range people start depending on private modes of transport Note: In practice both first mile & last mile are synonymously used Thus Unavailability of LMC options acts as deterrent to use of Mass transport

5 LITERATURE STUDY PARIS SEOUL HONGKONG Seoul MRTS Network Singapore Cycle network for LMC LONDON SINGAPORE SAN FRANSISCO MELBOURNE Findings for LMC requirement Multiple modes availability Extensive network Less waiting time Seamless travel between MRT & LMC modes Reduction in pollution

6 DELHI POPULATION GROWTH TREND DELHI Rs 19, Rs. 40, ,426 premature deaths/year in India i.e. 100% increase capacity to buys due to air pollution pvt vehicle increasing rapidly Population (lakhs) AIR POLLUTION PER CAPITA INCOME DELHI Source: Statistical abstract of Delhi 2014, VEHICLE GROWTH TREND 2014 INCREASE IN ROAD NETWORK IN DELHI Source: SAFAR, : 3,90,000 veh /million pop - Highest registered vehicles compare to global cities Source: Statistical abstract of Delhi 2014, IUT 2014 Source: State of Environment Report for Delhi 2010

7 PUBLIC TRANSPORTATION SCENARIO IN DELHI DELHI CHANGE IN MRTS RIDERSHIP PATTERN PCTR MRTS DELHI MASS PUBLIC TRANSPORTATION BRTS SuburbanRail PRIVATE MODES IPT AUTO CYCLE RICKSHAW MODAL SPLIT DELHI 2014 CAR Daily Avg. Ridership Growth MRTS (25 lakh people) 2 WHEELER FUTURE SCENARIO MAIN HAUL 140 kms Loop rail CURRENT SCENARIO 49% 66% 39% This change in Ridership depends on MAIN HAUL km 138 stations 3000 daily trips 3-4 min frequency + 72 lakh MRT/BRT 102% 72% Characteristics of stations LAST MILE- FEEDER BUSES 117 bus 24 stations (17% of all) + + Ridership TRAVEL DEMAND Delhi- 26 million trips LAST MILE- CYCLE Hire a cycle scheme Vishwavidyalaya Cycle Sharing LAST MILE- WALKING 60% area within 5 mins walking Annual travel demand in increasing at the rate of 9.5% IN DELHI TRANSPORT DEMAND FORECAST BY RITES 2020 Share of Mass transit is below Mode Daily Trips 2021 Modal share % desired range Modal split growing opposite Car OPERATION HOURS : 5:30 am - 00:00 hrs (17 hrs) Ideal for Delhi Mass transit 2w DAILy 000 trips at 3-4 min frequency should be at least 75% (>10 ROUTE LENGTH: 138 stations Auto kms connecting million population) Bus Metro Intracity Train Total Decongesting Delhi, IUT 2014

8 CHARACTERISTICS OF STATIONS LANDUSE Residential DENSITY RIDERSHIP Very Low Ridership OBJECTIVE Commercial Mixed PSP Low Density Mid Density High Density Low Ridership Mid Ridership High Ridership Develop a matrix based on station characteristics where certain set of stations represent similar characteristics Industrial Very High Ridership

9 EVOLVING STATION TYPOLOGY STEP 1- LANDUSE & DENSITY HIGH DENSITY MID DENSITY Step 2- LANDUSE & RIDERSHIP HIGH MID RIDESRHIP RIDERSHIP LOW DENSITY LOW RIDERSHIP Distribution of station w.r.t landuse, density & Ridership RESIDENTIAL Residential COMMERCIAL PSP INDUSTRIAL MIX TOTAL Commercial PSP Industrial Mix Step 3 - STATION MATRIX w. r. t LANDUSE & DENSITY & RIDERSHIP HIGH DENSITY MID DENSITY LOW DENSITY RESIDENTIAL COMMERCIAL PSP INDUSTRIAL MIX TOTAL Note: The matrix consists of mid value stations within the category Note: Staions in the matrix excludes stations w.r.t special /Occasional/CBD /Industrial Area Stations

10 Case Station Identification & Data Collection Source : UNEP Data 2014, SPA TP Thesis Overall Delhi Across Metro Stations of different character TRIP CHARACTERISTICS w.r.t Landuse Ridership Across Trip producing & Trip attracting Areas 9 stations & Hauz Khas & Noida SURVEYS SURVEY TOOLS Station Area Inventory Quantitative Transport Supply Status Survey Formats Questionnaire Survey TVC at Station User Characteristics Qualitative Trip Characteristics Ranking & rating CAI Walkability App User Attitudinal Survey Walkability DETAILED STUDY SAMPLE STUDY 17 Days 700 samples Trip Characteristics, User Characteristics, Availability of modes Transport Supply Status, User Attitudinal Survey Walkability, Station Area, TVC LOCATION 9 locations Hauz Khas, Noida Hauz Khas

11 LMC TRIP CHARACTERISTICS DELHI NCR 9 cardinal Zones Green Mode Public Transport Carbon Mode 43% 28% 29% Use of private vehicles is almost same all over Delhi UNEP Data 2014 ACROSS 9 STATIONS TRIP Stations at Trip Length Time Cost Attracting areas Producing areas Trip producing areas has longer trip lengths TRIP CHARACTERISTICS across USER GROUPS ATL (km) ATT (min) ATC (Rs) Female Male Regular Trip maker Ocassional Trip maker Though ATL & ATT of male is more ATC is similar for both Regular trip maker tends to spend less time and cost for a lesser distance w.r.t to occasional trip maker

12 LMC Trip Attributes at Case Stations HAUZ KHAS OCCUPATION OF USERS NOIDA SECTOR 15 LMC Modal Split Hauz khas is one of the 17 station with feeder bus TRANSPORT SUPPLY STATUS Supply Attributes Station HAUZ KHAS NOIDA 15 Mode Location DTC Bus Bus bay Feeder Bus No designated location DTC Bus Bus Stop Supply Distance (m) Frequency Avg. Pick up Waiting Passenger time (min) Time (min) pick up

13 STATISTICAL TEST OF TRIP ATTRIBUTES FINDINGS SUMMARY STATISTICAL TESTS MEAN DESCRIPTIVE STATISTICS STANDARD DEVIATION HAUZ KHAS NOIDA COEFFICIENT OF VARIATION ANOVA TEST Ratio OF LAST MILE TRIP TO TOTAL TRIP Length VARIATION Time Cost NO VARIATION On Mean Across Stations SIMILAR Mean At Trip end SIMILAR Mean By User DIFFERENT Mean By Mode DIFFERENT Residential LONGER LMC Institutional SHORT LMC

14 LMC Trip Attribute Across Case Stations Last Mile Trip Length Access & Dispersal trip are similar Dispersal trip are shorter HAUZ KHAS Most trips are within 6 mins of travel Longer waiting time Last Mile Trip Cost Source: Primary survey 2015 Dense job distribution around Noida station NOIDA SECTOR 15 Last Mile Trip Time

15 Service Area Determination of Modes SERVICE RANGE OF MODES MODES Walk Cycle Auto Bus PVT (km) (min) >3 > TRIP RATIO ANALYSIS Mode of travel Walk Bus IPT PVT ATL ATT LMC % MAIN HAUL % LMC % MAIN HAUL % % Distribution of Trip as per Service Range of Mode Range WALK AUTO BUS GRAMIN SEVA RICKSHAW Primary Catchment km Secondary Catchment km 3-5 km *Note: Entire table sums upto 100% W CAR

16 Existing Situation Assessment Primary catchment Area WALKABILITY ASSESSMENT AT HAUZ KHAS HAUZ KHAS METRO CATCHMENT AREA Locati on D D C A B C Pedestrian Network Length = 2.8 km Physical infrastructure Speed (m/sec) LOS Y 0.60 F 200 Y 0.90 E D E Width (mts) length (mts) Frequent obstruction A B 1.5 C D Avg E LOS Improvement required WALKING DEPENDS UPON A Pedestria n flow B Source: Author, 2015 NOTE: Representation (not to scale) Source: User preference survey, 2015 An empirical Study conducted at various station also shows that people walk more at areas with better pedestrian infrastructure Avg Indian condition has LOS C Source: Dr. Purnima Parida, Scientist, CRRI, 2009 & Primary survey at 7 stations, 2015

17 Existing Situation Assessment Secondary catchment Area HAUZ KHAS METRO AREA Gramin Sewa queue blocking DTC Bus Stop Feeder Bus & Auto waiting to pickup passenger PT ROUTES WITHIN 5.5 KM RADIUS OF HAZU KHAS STATION 22% trips Too long queue depicts unmatched demand supply status Choked traffic in front of Staion 12% trips 20% trips 46% trips BUS STO P NOTE: Representation (not to scale) HAUZ KHAS EXIT BUS AUTO CAR DTC BUS GRAMIN SEWA Source: Author, 2015 Mode Location Supply SATISFACTION OF LAST MILE MODE CHOICE 14 % BUS USERS Feeder BUS 40% AUTO USERS DTC Bus Bus bay Feeder Auto Hours of Frequency/ Avg. Passenger operation 15 min pick up Need for Improvement Unmatched demand supply status Long waiting time acts as a deterrent to use of service not satisfied with availability & waiting time Auto users are least satisfied with fare Thus an option to increase availability of buses might lead to shift of auto users dissatisfied with High fares Source: User preference survey, 2015

18 SHIFT POSSIBLE? Carbon modes PM 2.5 per person gm/km CO per Person gm/km How 58% 39% 70% 27% VS Sustainable modes PM 2.5 per person gm/km CO per Person gm/km person person 80 person Underlying FACT : A user will only shift if he has option within his range of travel

19 EXPECTED MODAL SHIFT LMC Modal Split at Hauz Khas Bus & Auto Walk & Auto SERVICE RANGE GRAPH Thus % Trips % Trips SERVICE RANGE GRAPH EMPIRICAL STUDY SAYS People walk more at areas with better pedestrian infrastructure Thus there can be a Probability of shift of users from Auto to walk within 1.5 km if walkability of the area is improved LOS Improvement by EMPIRICAL STUDY SAYS Ridership increases with increase service availability in reducing waiting time through improving frequency of to check probability for shift from Auto to Sustainable public transport mode Bus. Development of Last mile access choice model equations

20 LAST MILE ACCESS CHOICE MODEL Equation 1 Probability of shift from Auto to Walk U (Auto) = (Time) (Cost) (W_Time) Model Data Share S_Time S_Cost S_W Primary catchment Area Validation Utility 75% % Walk Auto % % % % Mode Cou nt User Ratio Model Ratio Walk % 26.30% Auto % 73.70% Equation 2 Probability of shift from Auto_Bus Here Share of Walk to Auto is 73.7 : 26 and data mismatch is 5% (73.7%-69.7%). Secondary catchment Area U(Bus) = (-0.07)(Time) + (-0.11)(Cost) + (0.261)(W_Time) Model Validation Data Share S_Time 75% % S_Cost S_W Utility Walk % % Auto % % Mode Coun t User Ratio Model Ratio Bus % 70.30% Auto % 29.70% Share of Bus to Auto is 30: 70 and data mismatch is very less i.e. 11%. (29.7%-40.9%).

21 SCENARIO BUILDING SCENARIO 1 SCENARIO 2 INCREASING WALKABILITY WITH EQUATION 1 OPTION Speed w.r.t S_Time S_Cost S_W DEVELOPMENT OF IMPROVED TRANSIT CONNECIVITY WITH EQUATION 2 Utility - Share Walk Auto Existing LOS E % 31% Option W1 LOS D % 30% Option W2 LOS C % 27.2% Option W3 LOS A,B % 27.3% OPTION Reduction in S_ S_Time S_Cost waiting time W Existing Option T1 Option T2 Option T3 15% 30% 60% MODAL SPLIT OPTIONS Auto Walk Bus Gramin sewa Pvt. Veh IMPROVED WALKABILITY Option W1 Option W2 Option W Walk % Share Bus Auto 26% 74% 32.9% 67.1% 37.2% 62.8% 57.9% 42.1% Service & Frequency Improvement Pedestrian infrastructure improvement BAU Utility MODAL SPLIT OPTIONS BAU Auto Walk Transit Connectivity Option T1 Option T2 Option T The share of bus increases with decrease in waiting time Bus Gramin sewa Pvt. Veh Bus Users 29% 37%

22 SCENARIO BUILDING SCENARIOS IMPROVED WALKABILITY SCENARIOS 33.5 % COMPOSITE OF WALK & TRANSIT COMPOSITE 1 IMPROVED TRANSIT CONNECIVITY SCENARIOS 29% COMPOSITE 2 37% Thus 5 modal split options

23 SCENARIO EVALUATION SCENARIOS Immediate influence area 5 MODAL SPLIT OPTIONS Secondary influence area 5 5 O P T I O N S 2 1 Walking LOS pedestrian amenities & Design standard Reduced waiting time by 30% 4 = 3 Reduced waiting time by 60% Reduction of emission levels can measured translating modal split w.r.t emission coefficients as given in the CMP toolkit,2015 by IUT Improved Walkability n waiting time = Improved Walkability n waiting time Improvement in scenarios led to reduction in motorized modes and increase in sustainable modes

24 Scenario Evaluation w.r.t Emission Reduction BUS 37% Improved Transit connectivity Walk 33 % BUS 29 % Emission level w.r.t Scenario 2 Source emission calculation: Vehicle occupancy is taken as per survey Emission coefficient is considered from CMP Toolkit Emission level w.r.t Scenario 3 Improved Walkability & Transit Walk 33 % Improved Walkability Emission level w.r.t Scenario 1

25 Scenario Evaluation w.r.t Emission Reduction EMISSION IN BAU SCENARIO Year Ridership ,171 68,434 1,07,432 PM 2.5 (gm/km) CO (gm/km) % 160% 46,066 70% 160% REDUCTION IN EMISSION WITH DEVELOPED SCENARIOS Scenario Share of Non Carbon Modes At BAU, considering same trend of modal split for future, the emission level for 10 years from now will grow up to 160 times higher How to achieve this Emissions Share of Carbon PM 2.5 Co modes (gm/km) (gm/km) 1 Walkability % -12% 2 Transit A % -16% 3 Transit B % -24% 4 Composite A % -27% 5 Composite B % -36%? Share of mechanized modes increase carbon emissions directly Option 5 gives 36% emission reduction Area level intervention

26 WALKABILITY IMPROVEMENT- PLANNING INTERVENTION PLANNING FOR PEDESTRIAN NETWORK WITHIN PRIMARY CATCHMENT AREA PEDESTRIAN NETWORK IDENTIFICATION CRITERIA Existing OD based demand Current Walking pattern Missing links Demography distribution Identifying commercial, recreational and residential areas Evaluating identified network by Graph Theory Increase in network from 2.8 km to 11.2 km

27 WALKABILITY IMPROVEMENT- PLANNING INTERVENTION Existing Network 2.8 km Graph Theory CONNECTIVITY (higher index is better) Connectivity Index Network Existing Proposed link (e) nodes (v) ACCESSIBILITY (lower index is better) Beta index (link/nodes) Gamma index Alpha Index HIGH All three indexes of Connectivity measure, namely Beta, Gamma and Alpha index show higher values for recommended network, thus indicating it as a better network. Accessibility Index Shimbel s index Associate number Mean Associate no Existing Proposed LOW Accessibility index gives a decrease in both Shimbel s index and associate number shows a lower value for recommended network. The mean associate number is also reduced by 40%, thus indicating network with improved levels of accessibility Recommended Network 11.2 km

28 TRANSIT CONNECTIVITY IMPROVEMENT PLANNING INTERVENTION Planning For Feeder Bus Services within Secondary Catchment Area FEEDER ROUTE IDENTIFICATION CRITERIA Existing OD based demand Catchment population service Existing service gaps Present Bus users Target users

29 TRANSIT CONNECTIVITY IMPROVEMENT PLANNING INTERVENTION Physical Evaluation of FEEDER TRANSIT SERVICES Current The physical performance of the transit is evaluated based on current practice standards 10 feeder PRACTICE STANDARDS Capacity MINI Bus Speed Load factor/day Vehicle utilization (km/bus/day) Financial performance of Feeder Transit services FU/day/route Operating cost (km/bus/day) Person/Bus/yr Fare (current) 5km & >5km kmph Rs Rs 5 & Rs feeder buses 90% user Considering same fare/km Increasing service standards leads to profit in spite of - increasing buses - Reducing route lengths which gives an improved frequency of 5 mins, improved fleet size from 24 and which suggests that it can serve 80% of users compared to 20% in the existing system Operational Profit Evaluation Current 20% Profit

30 RECOMMENDATION SERVICE RANGE LMC Recommendations 800 m upto km 3-5 km Beyond 3.5 km Walk, Cycle Walk, cycle, Rickshaw, E rickshaw Rickshaw, E rickshaw, Auto, Feeder Auto, Feeder Bus Private vehicles MODAL SPLIT for (Low density High Ridership Stations SERVICE RANGE MODAL SPLIT SUPPLY/ 1000 user RECOMMENDED feeder GUIDELINES Auto Walk Bus Gramin sewa Pvt. Veh Modal Split SUPPLY/ 1000 user (Low density High Ridership Stations Auto Auto Bus Gramin sewa 21% RECOMMENDED feeder GUIDELINES Route Network frequency Bus: metro Capacity VU (km/bus) Fare 3-8 km 5 7 mins 1: Rs10 (<5km), Rs 20(>5km)

31 LMC APPROACH RECOMMENDED 2 1 Existing Demand assessment Development of Last Mile access choice model LAST MILE MODAL SPLIT 4 3 Station catchment Area Audit STATION AREA DEVELOPMENT PRIMARY STATION AREA SECONDARY STATION AREA 5 Improvement Identification Application of Model to Identify Best Strategy 6 Planning Intervention In Catchment Area MULTIMODAL INTEGRATION Physical Integration (Route synchronization) Operation Integration (synchronization of time) Fare Integration (One smart card) Information Integration

32 CONCLUSION LMC improvement Impact at Hauz Khas Station area FEEDER SYSTEM Improvement in MODAL SPLIT

33 WALKABILITY IMPROVEMENT Increase in pedestrian Network CONCLUSION Maximum walking distance to metro is 800 mts All Residential & Recreational connected TRANSIT CONNECTIVITY IMPROVEMENT Waiting Frequency Fleet size time increase decrease Financial Gain User served Improvement of Multimodal Integration TRANSPORT SUPPLY & METRO 5 MIN INTERVAL 2 metro (approx.) 1 feeder bus 2 min Waiting Time 45 Auto 2 Gramin sewa vehicle 3% USER BENEFIT ASSESSMENT Feeder Services Existing Recommended No of routes User served Fleet size 1 20% % 26 Frequency Waiting Time 15 min 8 min 5 min 2 min 14 % 37% 25 % 6 6% % 21% 52 % 33%

34 MAIN LINE HAUL OF TRAVEL Any Questions?