An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville

Over 400 cities around the world have deployed or have plans to deploy a bike sharing system. However, the factors that drive their usage and the amount of rebalancing they require are not known precisely. A knowledge of these factors would allow cities to design or modify their systems to increase usage while lowering rebalancing costs. We collect station-level occupancy data from two cities and transform station occupancy snapshot data into station level customer arrivals and departures to perform our analysis. Specifically, we postulate that arrivals and departures from stations can be separated into: (i) arrivals (and departures) due to consumers, and (ii) arrivals (and departures) due to the system operators for rebalancing the system. We then develop a mixed linear model to estimate the influence of bicycle infrastructure, socio-demographic characteristics and land-use characteristics on customer arrivals and departures. Further, we develop a binary logit model to identify rebalancing time periods and a regression model framework to estimate the amount of rebalancing. The research is conducted using bike sharing data from Barcelona and Seville, Spain. The resulting modeling framework provides a template for examining bicycle rebalancing in different contexts, and a tool to improve system management of bicycle sharing systems.