New methods of space-time data analysis to operationalise passively collected transport data on the route network

Cycling has the potential to tackle multiple interrelated problems including sedentary lifestyles and the associated obesity crisis, air pollution and road traffic congestion. In recognition of its benefits for the environment, society and economy, Government aims to double cycling by 2025 (Department for Transport 2017).

To do so depends on a number of interventions, including investment in safe, convenient and joined up cycling networks. A growing evidence-base demonstrates how this can happen. The Propensity to Cycle Tool (PCT) and Cycling Infrastructure Toolkit (CyIPT) projects, key components of which were developed at the University of Leeds, operationalise this data, providing a vital tool for local authorities and other stakeholders investing in cycling (Lovelace et al. 2017). However, a problem with these tools is that they take little account of the quality of existing infrastructure. Specifically, road and cycleway smoothness and width are absent from both projects.

To overcome this problem new datasets are needed. The aim of the PhD will be to incorporate emerging datasets - such as those generated by See.Sense products - into transport planning, using new and innovative methods and new and emerging software for data science where appropriate.