Service Region Design for Urban Electric Vehicle Sharing Systems

Abstract

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles' special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib', car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go's service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.

Future mobility as a bio-inspired collaborative system

“…Vehicle-sharing allows the convenience of private vehicles with the efficiency of public transportation (He, Mak, Rong, & Shen, 2017). Research shows that sharing can reduce the number of vehicles on the roads (Martin, Shaheen, & Lidicker, 2010;Martinez & Crist, 2015;Millard-Ball, 2005).…”

“…Research shows that sharing can reduce the number of vehicles on the roads (Martin, Shaheen, & Lidicker, 2010;Martinez & Crist, 2015;Millard-Ball, 2005). Since the cost of ownership is shared, it can also promote the use of electric vehicles, which have a high purchasing cost but low operating costs (He et al, 2017;Jones & Leibowicz, 2019).…”

A Review of Robust Operations Management under Model Uncertainty

“…Robust optimization is applied to address the uncertainty in users' residential and work address relationships. He et al (2017) investigated carsharing service system planning under significant uncertainty in user adoption behavior. They assumed users gain utility from service coverage at different locations and adopted the moment model to address distributional ambiguity of user utility.…”

Competition and coordination strategies of shared electric vehicles and public transportation considering customer travel utility

“…With the gradual rise and development of SEV, there has been an upsurge of scholarly attention on the research of operational problems of SEV. The majority of the studies focus on the following three problems: rebalancing or scheduling problem (Bruglieri et al 2014;Weikl and Bogenberger 2015;Li et al 2016;Correia and Antunes 2017), charging problem (Dong et al 2014;Avci et al 2014;Neubauer and Wood 2014;Wu 2019;Paula et al 2020) and station selection and station planning (He et al 2017;Kaspi et al 2016;Mak and Shen 2014;Ang et al 2012). After years of research on the above problems, it has been found that the impact of other modes of transportation on SEV as well as the coordination problem between them should be considered.…”