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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

mailto:[email protected]


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


Vehicle models have existed since the 1940s


Modern vehicle models don’t work for cycling, though,

as they use a simplified network with many streets missing


Zone sizes are also too large – larger than many active travel trips


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


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


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


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


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


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


Unlike in Cardiff, the proportion cycling to work is not

explained by urban density alone


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


Combining the effect of slope and traffic we start to see why

south east Newport has the highest proportion cycling to work


Example of a decision support tool:

Predicting which mid length cycle trips use a designated piece of infrastructure


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

sdna - modelling cyclist behaviour based on welsh …...• modelling cyclist behaviour based on...

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