Vehicle Rebalancing in a Shared Micromobility System with Rider Crowdsourcing

Abstract

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL. Funding: This work was supported by the Research Grants Council of Hong Kong [Grants 15501319 and 15505318] and the National Natural Science Foundation of China [Grant 71931009]. Z. Jin was supported by the Hong Kong PhD Fellowship Scheme. Y. F. Lim was supported by the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1199 .

A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era

“…Eccarius et al, through an examination of user behavioral psychology, demonstrated that a lack of personal values and mobility needs may reduce students' intention to use green transportation services [8]. In addition, research has been conducted on such issues as the operation, safety, and subjective well-being related to shared micromobility [9,10]. However, the existing research on shared micro-mobility predominantly focuses on specific areas such as user behavior characteristics or corporate operations, lacking a comprehensive exploration of factors influencing shared micro-mobility satisfaction in the product dimension.…”

“…Shared micro-mobility should provide users with diversified service experiences, rather than just a single product [8]. For the enterprises that provide shared services, an effective way to enhance user satisfaction is to provide diversified functions and a rich traveling experience [9]. Under this premise, acquiring a deep understanding of users' perceived values in the context of shared micro-mobility and revealing the influencing factors and deep mechanisms behind the perceived value and user satisfaction of products have become urgent requirements for making objective assessments of the development of shared micro-mobility and optimizing marketing strategies.…”