Cynemon - Cycling Network Model for London
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1 1 MARCH 2017 Cynemon - Cycling Network Model for London Aled Davies TfL Planning
2 2 Background Investment in cycling has increased significantly in recent years We need robust appraisal to justify cycling investment Solution: Cycling Demand Response (CYDER) framework LTS Multi-modal London-wide Cynemon Cyclist Route Choice and Demand Response HAMs Sub-Regional Highway WebCAT Online Connectivity Mapping
3 3 Cynemon Cycling Network Model for London New cycling model which estimates cyclist routes, journey times and flows at a strategic level across London for scheme and policy appraisal Which are the busiest cycling corridors? Where do cyclists using specific roads come from and go to? What is the journey time between A and B? Which are the preferred routes between A and B? What is the strategic impact of cycling demand growth on cycle flows? What is the strategic impact of infrastructure on cycle flows and journey time?
4 4 How has it been developed?
5 5 Network built from many sources ITN + Urban Paths network structure: Additional attributes from: TfL GIS Layers OpenStreetMap STRAVA TfL App 5
6 6 Cycling demand estimated from a combination of traditional and new sources Core LTDS Census JTW Cycle Hire OD Additional TfL IRR OD Central London Termini OD Validation Other OD surveys STRAVA TfL App Cycle counts
7 7 Data collection through online survey and mobile phone app to inform route choice algorithm Observed routes were used to determine the value that cyclists place on: - Road type - Cycle infrastructure - Bus lanes -Traffic volumes -Gradient
8 8 Around 2,500 counts used to calibrate the model 8
9 9 What insight can it offer?
10 10 Base year flows (all cyclists) South-West prominence Cycle Superhighways
11 11 Trips using Waterloo Bridge 11
12 12 Cynemon Outputs Cycle Flow Cycle Journey Times Routing Monetary Benefit
13 13 Cynemon Forecast Model
14 14 Key Features Model Base Year: 2014 Future Years: 2021, 2031, 2041, (2026) Time periods: AM Peak (08:00-09:00) Inter Peak (Average hour 10:00-16:00) PM Peak (17:00-18:00)
15 15 We have the following standard forecast scenarios: Scenario Name Year Network Demand Potential Use Reference Case Committed and funded schemes Future behaviour MTS, scheme tests/appraisal Target Case 2026 Committed and funded schemes 1.5m cycle stages - Strategic Case Investment Plan Future behaviour Strategic Cycling Network Plan
16 16 Cynemon Demand Forecasting Process Base Year Demand Population/employment growth Background growth Other modes forecast demand: Railplan & LoHAM Scheme impact estimate (adapted from CYPET) Scheme impact Switchable Trips Push factors from highway and PT Push Factors Forecast Demand LTDS: % of potentially cyclable trips/journeys (borough level) Current or future behaviour Back-casting exercise Unexplained Factors Other factors currently unexplained
17 17 Growth in Cycling due to Population & Employment (Includes effect of changing age distribution) More growth in east London 2014 to 2026
18 18 Growth in Cycling due to Scheme Response A continuation of existing infrastructure with new ones will boost cycling growth 2014 to 2026 More growth occurs in areas with denser concentration of cycling infrastructure
19 19 Growth in Cycling due to Push Factors (Includes fuel prices, fares and journey times by other modes) Increased journey time for highway and PT will push people to cycle 2014 to 2026
20 20 Growth in Cycling due to Unexplained Factors The unexplained factors contribute about 35% of total growth There is a potential to unlock new cycling trips in the north and north-east area 2014 to 2026 The distribution of new cycling trips is based on potentially cyclable trips, the propensity to cycle segment analysis, and current cycling trip length
21 21 Next Steps
22 22 Next Steps Variety of scheme applications in TfL and externally Understanding insights provided by new forecasting approach Cynemon P2
23 23 Questions?
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