Data-Driven Analysis of Optimal Repositioning in Dockless Bike-Sharing Systems

Abstract

Bike-sharing systems provide sustainable and convenient mobility services for short-distance transportation in urban areas. The dockless or free-floating bike-sharing systems allow users to leave vehicles at any location in the service zones which leads to an imbalance of inventory between different areas across a city. Hence, vehicles in such dockless bike-sharing systems need to be repositioned throughout the day to be able to capture and serve more demand. In this study, we analyze the impact of optimal repositioning on the efficiency of dockless bike-sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of the bike-sharing system. The proposed model is then implemented on the real-world bike-sharing data of New York, Toronto, and Vancouver. After finding the optimal repositioning actions, we analyze the effect of repositioning on the fulfilled demand, the number of required vehicles, and the utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of bike-sharing systems under these performance measures. In particular, our analyses show that up to 41\% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size up to 61\% and increase the average utilization rate of the vehicles up to 21\% by employing repositioning. We also demonstrate that the effect of optimal repositioning is robust against the uncertainty of demand.