The benefits of cycling for improving public health and environmental sustainability have been well established, and many countries have built new cycling infrastructure to encourage people to cycle more.
However, the effectiveness of such infrastructures has not been well examined, mostly due to data limitations, making it difficult for policy makers to judge the real value of this form of investment. In addition, the lack of detailed cycling data limits our understanding of cycling behaviour (e.g., weather effects, route choice, effects of unprecedented events, etc). 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
- Examine changes in cycling patterns due to COVID19, and the role of safe cycling infrastructure in post-pandemic cities
We will use multi-year crowdsourced data from Glasgow and Edinburgh, built environment factors (e.g., land use, infrastructure), weather data and other relevant datasets, and utilise small area estimation techniques and/or machine learning to predict cycling activities at the fine geographical scale. Data from sensors and existing models (e.g., those used by the Department for Transport) will be used as ground truth measures to calibrate and validate our models. Advanced statistical models (e.g., fixed effects spatial panel models) will be employed to evaluate the effects of cycling infrastructure as well as other interventions,including the coronavirus pandemic, and how these vary between Glasgow and Edinburgh.
This project involves several public organisations. For instance, we are currently working with Sustrans, Glasgow City Council and City of Edinburgh to evaluate cycling networks with new forms of data. 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.
- Glasgow City Council: Assisting with data collection and dissemination of results.
- City of Edinburgh Council: Assisting with data collection and dissemination of results.
- Paper: Raturi, V., Hong, J., McArthur, D. and Livingston, M. (2021) The impact of privacy protection measures on the utility of crowdsourced cycling data. Journal of Transport Geography, 92, 103020. (doi:10.1016/j.jtrangeo.2021.103020)
- Paper: Hong, J., McArthur, D. and Raturi, V. (2020) Did Safe Cycling Infrastructure Still Matter During a COVID-19 Lockdown? Sustainability. 2020; 12(20):8672 (doi:10.3390/su12208672)
- Active Travel Podcast: data in active travel, part one, featuring Dr David McArthur
- Paper: Livingston, M., McArthur, D., Hong, J. and English, K. (2020) Predicting cycling volumes using crowdsourced activity data. Environment and Planning B: Urban Analytics and City Science, (doi:10.1177/2399808320925822)
- Paper: Hong, J., McArthur, D. P. and Stewart, J. (2020) Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava). Transportation Research Part A: Policy and Practice, 133, pp.109-121 (doi:10.1016/j.tra.2020.01.008)
- Paper: Hong, J., McArthur, D. P. and Livingston, M. (2019) The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data. Transportation, pp 1–14. (doi:10.1007/s11116-019-09988-4)
- 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