• www.cardiff.ac.uk/sdna
Modelling cyclist behaviour based on Welsh Active Travel data
Dr Crispin [email protected]
Presentation to National Assembly for Wales Members8th March 2016
Sustainable Places Research Institute
Department of Planning and Geography
• www.cardiff.ac.uk/sdna
Adapting transport models for pedestrians and cyclists
In the context of a wider decision making framework, models can help to
• Estimate cost benefit ratios– Environmental
– Social (community cohesion, health)
– Economic
• Compare options on the table
• Identify where new schemes are most needed
Understand current activity
Identify barriers to adoption
Maximise potential for future
usage
• www.cardiff.ac.uk/sdna
Vehicle models have existed since the 1940s
• www.cardiff.ac.uk/sdna
Modern vehicle models don’t work for cycling, though,
as they use a simplified network with many streets missing
• www.cardiff.ac.uk/sdna
Zone sizes are also too large – larger than many active travel trips
• www.cardiff.ac.uk/sdna
Our cycle models work at individual link level to avoid zones
All links are included and the model is 3d to account for cyclists avoiding hills
• www.cardiff.ac.uk/sdna
Previous research: a Cardiff cycling model based on Open Street Map
The first step is to simulate vehicle traffic as cyclists will avoid it where possible
• www.cardiff.ac.uk/sdna
Explains 61% of cycle flows based on minimal data (network only, no demographics)
• Visual reference for integrated network planning
• Improve current models of cost benefit (HEAT)
• Can integrate with demographic models to improve explanatory power
• www.cardiff.ac.uk/sdna
sDNA conflict model
• Identifies 75% of incident sites to within 30m (75% sensitivity)
• Identifies 73% of safe sites (73% specificity)
• This validates the model
• Accidents are sparse so some incident-free roads which the model
thinks dangerous may just be lucky
• Accident data low quality which limits model performance in this test
Uses
• Baseline risk model
• Identify priority roads
for improvement
Data: DfT 2005-2012
• www.cardiff.ac.uk/sdna
ESRC Impact Accelerator
• Money for not doing research!
• Spent on collaboration with partners (Sustrans, private sector)
Descriptive tools
• Map of cycle travel times
• Visualise existing flows through network
• Map barriers to access
Intermediate tools
• Map ‘perceived effort’ of access by cycle
• Map effect of barriers to access
• Map potential users of given link
Full modelling
• Full demand model
• Predict impact of infrastructure
• Cost benefit ratios
• www.cardiff.ac.uk/sdna
Newport model based on Active Travel data
• Explains 65% of measured flows (again without demographics – include these for better fit)
• Better flow data collection will also improve models
• www.cardiff.ac.uk/sdna
Unlike in Cardiff, the proportion cycling to work is not
explained by urban density alone
• www.cardiff.ac.uk/sdna
Mapping the areas where desired routes are worst affected by
vehicle traffic helps understand what is going on
The worst affected areas could benefit from infrastructure projects
• www.cardiff.ac.uk/sdna
Combining the effect of slope and traffic we start to see why
south east Newport has the highest proportion cycling to work
• www.cardiff.ac.uk/sdna
Example of a decision support tool:
Predicting which mid length cycle trips use a designated piece of infrastructure
• www.cardiff.ac.uk/sdna
Where next?
Accurate, current data (ATA is a good start!): infrastructure, monitoring, flows, users
Models and decision support tools
Identification of barriers to uptake
Changing attitudes & further adoption
Healthier, greener, more prosperous society