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

骑自行车有可能解决多种相互关联的问题，包括久坐不动的生活方式和相关的肥胖危机、空气污染和道路交通拥堵。认识到其对环境、社会和经济的好处，政府的目标是到2025年将自行车加倍（交通部2017年）。要做到这一点，需要采取一系列干预措施，包括投资于安全、方便和联合的自行车网络。越来越多的证据表明这是可能发生的。由利兹大学开发的骑行倾向工具（PCT）和骑行基础设施工具包（CyIPT）项目将这些数据付诸实施，为地方当局和其他投资骑行的利益相关者提供了重要工具（Lovelace et al. 2017）。然而，这些工具的一个问题是它们很少考虑现有基础设施的质量。具体来说，道路和自行车道的平整度和宽度都没有出现在这两个项目中。为了克服这个问题，需要新的数据集。博士学位的目标将是整合新兴的数据集——比如由See生成的数据集。感知产品-运输规划，使用新的和创新的方法和新的和新兴的软件数据科学在适当的地方。

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.