Optimizing Bike Rebalancing via Spatial Crowdsourcing: A Matching Approach

Bike sharing systems are a new form of public transportation where users are allowed to take out and return bicycles using various stations throughout the city. While such a system is innovative, and has solidified its prevalence to the public, it is still in its infancy with many improvements yet to come. One of the largest issues present is the imbalance of the Bike Sharing System (BSS), or more broadly ridesharing systems, the unavailability of bikes or empty parking spaces in areas with a high density of users. In this paper, we propose a spatial crowdsourcing approach where users receive monetary incentives to rebalance bikes by returning bikes to stations that need it rather than users' intended locations to improve the system's overall bike utilization. However, how to determine the best incentive mechanism is challenging. We formulate this problem into an optimal matching problem and convert it into a minimum-cost flow problem to find the best way to choose which stations to rebalance and the optimal rebalancing amount. To demonstrate the effectiveness of the proposed method, we validate our approach using D.C. Capital BikeShare data and extensive simulation shows that our approach on average can improve the efficiency and cost of simple greedy algorithms by 32.1%.