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。认识到自行车对环境、社会和经济的好处，政府的目标是到2025年将自行车数量增加一倍(2017年交通部)。要做到这一点，需要采取一系列干预措施，包括对安全、便利和连接的自行车网络进行投资。越来越多的证据表明了这种情况是如何发生的。利兹大学开发了自行车倾向工具(PCT)和自行车基础设施工具包(CyIPT)项目，这些项目的关键组件是利兹大学开发的，它们将这些数据付诸实施，为地方当局和其他利益攸关方投资自行车提供了重要工具(Lovelace等人)。2017年)。然而，这些工具的一个问题是，它们几乎没有考虑现有基础设施的质量。具体地说，道路和自行车道的平整度和宽度都不在这两个项目中，为了克服这个问题，需要新的数据集。博士学位的目的将是将新兴的数据集--如See.Sense产品生成的数据集--纳入交通规划，在适当的情况下使用新的创新方法和新的新兴数据科学软件。

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.