Comparing cities’ cycling patterns using online shared bicycle maps

Abstract

Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from ten cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future. We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.