Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes

Abstract

Understanding the interactions between different travel modes is crucial for improving urban transport resilience, especially during times of disruption and transit failure. As a flexible and sustainable travel mode, bikeshare schemes are able to solve “first/last” mile problems in urban transit as well as provide an alternative to motorised traffic. This paper uses OD (origin and destination) trip data from the London Cycle Hire Scheme and temporal docking station bike availability data to explore the impact of four separate London Underground (Tube) strikes on bikeshare usage and behaviours. The results suggest that bikeshare usage generally rises in response to Tube disruptions, but the extent and nature of this rise in use varies according to the type of disruption. A novel measure of station pressure suggests that the scheme very quickly reaches saturated capacity and is unusable in certain parts of London during disruptions. A graph-based analysis reveals several changes in OD flow structures. This implies a modal shift from Tube to bikeshare and a change of route behaviours among bikeshare users. Weekday Tube strikes bring new behaviours and new OD pairs to the bike flow structures, whilst for weekend strikes existing patterns are consolidated. The corollary is that more heterogenous OD trip patterns are introduced by higher volumes of commuting trips and intense competition of cycles/docks. Cyclists are forced into using alternative (second or third preference) docking stations with new behaviours, and possibly users, as journeys that would otherwise be made via the Tube are made via bikeshare. This work comprehensively presents and compares the impacts of Tube strikes under varied circumstances and offers a detailed understanding of the changed cycling behaviours that could be used in transport planning and management.

Publication
Journal of Transport Geography
Roger Beecham
Roger Beecham
Associate Professor in Visual Data Science

My research interests include data visualization, applied data science, geographical analysis and computational statistics.