How Does Dockless Bike-Sharing System Behave by Incentivizing Users to Participate in Rebalancing?

Abstract

Dockless bike-sharing has provided a flexible and convenient travel mode in recent years, with which shared bikes can be rented and returned easily. However, addressing the imbalance between demand and supply effectively becomes a major challenge to guarantee the sustainability of such systems. Vehicle-based approach results in substantial operating costs and more emissions. This study proposed a user-based model to incentivize users to participate in bike rebalancing. This model contains three important components: 1) determination of a bike surplus region by sorting surplus level in descend order; 2) determination of a bike deficient region from the perspective of users' utility maximization; and 3) updating the states of regions. Several numerical tests were implemented to evaluate the model's performance based on predefined parameters and historical trip data in downtown of Nanjing. As the results indicate, unmet demand can be greatly reduced if providing a certain level of monetary incentive. Different values of the maximum acceptable walking distance make little difference in demand loss and operating cost at a low level of incentive. With regard to the users, it implies that higher proportion of cooperative customers who are willing to participate in rebalancing can lead to a larger reduction of unmet demand. In addition, using surplus level as the index to define bike surplus regions is beneficial to saving the overall operating cost. INDEX TERMS Dockless bike-sharing system, bike rebalancing, user-based model, monetary incentive.

Station Importance Evaluation in Dynamic Bike-Sharing Rebalancing Optimization Using an Entropy-Based TOPSIS Approach

“…It also can be applied in user-based operation. Ji et al [76] have suggested that higher monetary incentive will encourage users to participate into bike-sharing rebalancing and significantly alleviate bike-sharing imbalance issue. Station importance evaluation can be helpful to find which stations should be rebalanced first by users.…”

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

“…However, the redistribution due to the imbalance of bike pick-up and return is more challenging for the dockless ride-sharing system. Maintaining a win–win situation for customers by providing monetary incentives in renting rides is highly effective in balancing the dockless ride-sharing system redistribution [ 39 ].…”

Learning incentivization strategy for resource rebalancing in shared services with a budget constraint

“…Duan and Wu [16] extended Pan et al's approach by learning an adaptive incentive mechanism for both renting from and returning to specific locations to solve the bike rebalancing problem under similar problem setting. Ji et al [17] proposed an incentive mechanism that maximizes service level and users' utility while encouraging users to return bikes to regions that have a shortage of bikes within an acceptable walking distance to destinations.…”