Characterising group-cycling journeys using interactive graphics


The group-cycling behaviours of over 16,000 members of the London Cycle Hire Scheme (LCHS), a large public bikeshare system, are identified and analysed. Group journeys are defined as trips made by two or more cyclists together in space and time. Detailed insights into group-cycling behaviour are generated using specifically designed visualization software. We find that in many respects group-cycle journeys fit an expected pattern of discretionary activity: group journeys are more likely at weekends, late evenings and lunchtimes; they generally take place within more pleasant parts of the city; and between individuals apparently known to each other. A separate set of group activity is found, however, that coincides with commuting peaks and that appears to be imposed onto LCHS users by the scheme’s design. Studying the characteristics of individuals making group journeys, we identify a group of less experienced LCHS cyclists that appear to make more spatially extensive journeys than they would do normally while cycling with others; and that female cyclists are more likely to make late evening journeys when cycling in groups. For 20% of group cyclists, the first journey ever made through the LCHS was a group journey; this is particularly surprising since just 9% of all group cyclists’ journeys are group journeys. Moreover, we find that women are very significantly (p < 0.001) overrepresented amongst these ‘first time group cyclists’. Studying the bikeshare cyclists, or bike share ‘friends’, that individuals make ‘first time group journeys’ with, we find a significantly high incidence (p < 0.001) of group journeys being made with friends of the opposite gender, and for a very large proportion (55%) of members these first ever journeys are made with a friend that shares the same postcode. A substantial insight, then, is that group cycling appears to be a means through which early LCHS usage is initiated.

Transportation Research Part C: Emerging Technologies, 47(2): 194–206
Roger Beecham
Roger Beecham
Associate Professor in Visual Data Science

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