Understanding cycling behaviour through visual analysis of a large-scale observational dataset

The emergence of third-generation, technology-based public bikeshare schemes offers new opportunities for researching cycling behaviour. In this study, data from one such scheme, the London Cycle Hire Scheme (LCHS), are analysed. Algorithms are developed for summarising and labelling cyclists’ usage behaviours and tailored visual analysis applications are designed for exploring their spatiotemporal context. Many of the research findings provide support to existing literature, particularly around gendered cycling behaviour. As well as making more discretionary journeys, women appear to preferentially select parts of London associated with greater levels of safety; and this is true even after controlling for geodemographic differences and levels of LCHS cycling experience. One hypothesis is that these differences represent diverging attitudes and perceptions. After developing a technique for identifying cyclists’ workplaces, these differences might also be explained by where cyclists need to travel for work and other facilities. An additional explanation is later offered that relates to the nature of cyclists’ estimated routes. The size and precision of the LCHS dataset allows under-explored aspects of behaviour to be investigated. Group cycling events – instances where two or more cyclists make journeys together in space and time – are labelled and analysed on a large scale. For certain types of cyclist, group cycling appears to encourage more extensive spatiotemporal cycling behaviour and there is some evidence to suggest that group cycling may help initiate scheme usage. The domain-specific findings, emerging research questions and also behavioural classifications are this study’s principal and unique contribution. A second contribution relates to the analysis approach. This is a data-driven study that takes a large dataset, measuring use of a relatively new cycle facility, and uses it to engage with research questions that are typically answered with very different datasets. There is some uncertainty around how discriminating and generalisable LCHS cycle behaviours may be and which variables,