E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing

The rise of dockless electric bike sharing becomes a new urban lifestyle recently. More than just the first-and-last mile, it offers a new modality of green transportation. However, in addition to the traditional re-balance and overcrowding problems, it also brings new challenges to urban management and maintenance. Due to the safety risks of batteries, customers are regulated to park at designated locations, which potentially causes dissatisfaction and customer loss. Meanwhile, service providers should charge those scattering low-energy batteries in time. To address these issues, we propose E-sharing, a two-tier optimization framework that leverages data-driven online algorithms to plan parking locations and maintenance. First, we balance the user dissatisfaction and the number of parking locations by minimizing their sum. To account for real-time dynamics while not losing track of the historical optimality, we propose an online algorithm based on its near-optimal offline solution. Second, we develop an incentive mechanism to motivate users to aggregate low-battery bikes together, saving the cost of bike charging. Our experiment based on the public dataset demonstrates that the online algorithm can minimize the cost from the conflicting objectives and incentive mechanism further reduces the maintenance cost by 47%.

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Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. “E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing.” IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 474-484, 2020.