A visual analytics approach to understanding cycling behaviour

Existing research into cycling behaviours has either relied on detailed ethnographic studies or larger public attitude surveys [1] [9]. Instead, following recent contributions from information visualization [13] and data mining [5] [7], this design study uses visual analytics techniques to identify, describe and explain cycling behaviours within a large and attribute rich transactional dataset. Using data from London's bike share scheme
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, customer level classifications will be created, which consider the regularity of scheme use, journey length and travel times. Monitoring customer usage over time, user classifications will attend to the dynamics of cycling behaviour, asking substantive questions about how behaviours change under varying conditions. The 3-year PhD project will contribute to academic and strategic discussions around sustainable travel policy. A programme of research is outlined, along with an early visual analytics prototype for rapidly querying customer journeys.