City Science was commissioned by Mott Macdonald (on behalf of Liverpool City Region Combined Authority) to develop the Liverpool City Region Walking & Cycling Model, to provide a better representation of walking and cycling. This reflects the City Region’s ambitions to deliver active travel schemes, such as those set out in their Local Cycling & Walking Infrastructure Plan (LCWIP). It integrates with an existing multi-modal strategic transport model and gives the Combined Authority a long-term platform for modelling active travel.
Our Walking & Cycling Model provides a step change in how active travel is modelled by:
- Developing a detailed understanding of existing and future walking and cycling demand by zone and link.
- Mapping a comprehensive network of existing active travel infrastructure and corridors, categorised by infrastructure type and its attractiveness to different user types.
- Producing evidence-based active mode travel data aligned with an existing strategic transport model.
- Providing research-based active travel costs to capture unobservable features, such as route attractiveness and topography.
- Forecasting demand for walking and cycling as a result of infrastructure changes and understand where on the network infrastructure and corridors this demand will be.
- Providing the foundation for a more robust evidence base to support and strengthen future government funding bids for new walking and cycling infrastructure.
City Science Response
We developed a bespoke Walking & Cycling Model that includes a detailed demand model for walking and cycling, a network of walking and cycling infrastructure and a python-based assignment model which considers the infrastructure and topographic conditions as a part of route choice. Our cost function is backed by extensive research into the route choice impacts of infrastructure types, route attractiveness and how these are perceived by different user types. This enables the modelling of more realistic model walking and cycling travel decisions, moving away from traditional shortest distance models that are based on journey time savings.
This model built on automated development processes including network development, which extracts data from OpenStreetMap to automatically create the network and assign costs to links. On top of the automated network builder, a “patching” system was developed to allow modellers to add further detail or adjustments to the network based on local knowledge or on-site observations.
Demand was implemented as a python-based gravity model, using travel surveys. The model prepares synthetic matrices using the trip ends created by the strategic model. Our model was calibrated using count data from sensors and third-party data (e.g. Strava) and is implemented for end-to-end running that can be provided as an API.
Our Walk & Cycle Model can be used to test new active travel interventions as well as the impacts of schemes for other modes on active travel. We will work with the Combined Authority to apply the model to understand the impact of proposed active travel interventions, to support upcoming funding decisions and business cases.
Since the model predominantly automatically generates network structures and input data based on open sources, the methodology can easily be transferred to any other study area. And as more research becomes available, we continually seek to develop and improve our Walk & Cycle Model, allowing improved representation of active travel demand and infrastructure.