The benefits of cycling (e.g., improving public health and making cities more active and environmentally friendly) have been well established, and many cities have built new cycling infrastructure to encourage people to cycle more.
However, the effectiveness of these investments has not been well examined, mostly due to data limitations. This project will tackle the issue by utilising diverse data sets and analytical approaches.
Aims and Objectives
The main aims of this project are to:
- Develop new analytical approaches to better utilise crowdsourced cycling data
- Evaluate cycling infrastructure investments as well as other policy interventions
- Investigate how their impacts vary between contexts and cities
We will use multi-year crowdsourced data from Glasgow and Edinburgh, built environment factors (e.g., land use, infrastructure) and weather data and utilise small area estimation techniques and/or machine learning to predict cycling activities at the output area level. Data from sensors and existing models (e.g., those used by the Department for Transport) will be used as ground truth measures to calibrate our models. Advanced statistical models (e.g., fixed effects spatial panel models) will be employed to evaluate the effects of cycling infrastructure and how these vary between Glasgow and Edinburgh.
This project involves several public organisations. For instance, we are currently working with Sustrans and City of Edinburgh to evaluate Edinburgh’s cycling network with new forms of data. We are also partnering with Sustrans in Glasgow to examine the impacts of the South City Way cycle route. The project will show how new cycling infrastructure, as well as other cycling policies, work in different spatial contexts. The results will help planners and policy makers to make more effective cycling policies with limited resources.
- Sustrans: Assisting in data collection and partnering with us for the evaluation of the South City Way cycle lane in Glasgow.
- Cycling Scotland: Assisting with data collection and dissemination of results.
- Paper: McArthur, D. P. and Hong, J. (2019) Visualising where commuting cyclists travel using crowdsourced data. Journal of Transport Geography, 74, pp. 233-241. (doi:10.1016/j.jtrangeo.2018.11.018)
- Blog (by Strava Metro): Urban Big Data Centre researchers show how Strava data can be used to predict cycling volumes as well as its potential use for cycling planning