User-based rebalancing approaches to free-floating bike sharing systems

Abstract

Bike sharing provides a flexible alternative for daily travel in modern cities, which allows people to use bicycles without owning one. Recently, we have seen rapid growth of an innovative sharing model named Free-floating Bike Sharing (FFBS). In comparison with traditional Station-based Bike Sharing (SBBS), FFBS increases user satisfaction because users can grab and park bikes at any valid places. However, the imbalanced distribution of bikes could result in low service quality. Hence, rebalancing operations are of great significance to the success of bike sharing. Rebalancing approaches can be divided into two categories: operator-based and user-based. The former employs staffs and trucks to move bikes, and the latter incentivizes users to help with rebalancing. Most previous research works focused on operator-based rebalancing, while very few studies considered user-based rebalancing approaches. Moreover, user acceptance of incentives has not been well studied yet. To this end, this thesis aims at solving the imbalance problem in FFBS using user-based rebalancing approaches. Four studies are performed to achieve the objective. First, this thesis proposes a Radiant Service Theory (RST) that captures the features of FFBS. Based on that, a user-based rebalancing approach, i.e., a one-stage dynamic incentive mechanism is introduced to induce users to change their destinations and return shared bikes to undersupply places. The pricing problem is formulated by a posted price model, in which users simply answer yes or no to given prices. Numerical studies are conducted to validate the rebalancing approach. Second, an incentive mechanism based on bidding model is investigated, in which users are allowed to bid for rebalancing tasks. The bidding model is solved by a dynamic online pricing method that is budget feasible, truthful, and could achieve near-optimal price. Computational studies show that the bidding model outperforms the posted price model in theory, but the advantage relies on user rationality. Third, the one-stage incentive mechanism is extended to a two-stage incentive mechanism. When users are only incentivized to change their origins or destinations, it is defined as a one-stage incentive mechanism. Contrarily, a two-stage incentive mechanism interacts with users at both pick-up and drop-off steps. A comparison of different incentive mechanisms is presented with the consideration of user preference. Fourth, users’ attitude towards incentives is addressed. To investigate bike sharing customers’ perceptions of incentive mechanisms and identify the determinants of their acceptance, technology acceptance model is extended by incorporating three variables that are perceived cost, perceived risk, and altruism. The proposed model is empirically tested using survey data from 403 bike sharing users. The contributions of this dissertation can be summarized as follows. From a theoretical perspective, it fills a research gap in the context of bike sharing rebalancing by proposing a series of user-based rebalancing approaches. Practically, the research results provide significant managerial insights for bike sharing operators to achieve higher service quality.