A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush-Kuhn-Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator's profit and the travelers' cost.

No abstract

Considering the characteristics of different types of users in hybrid carsharing systems, in which sharing autonomous vehicles (SAVs) and conventional sharing cars (CSCs) coexist, a tailored pricing strategy (TPS) is proposed to maximize the operator’s profit and minimize all users’ costs. The fleet sizes and sizes of SAVs’ stations are also determined simultaneously. A bi-objective nonlinear programming model is established, and a genetic algorithm is applied to solve it. Based on the operational data in Lanzhou, China, carsharing users are clustered into three types. They are loyal users, losing users, and potential users, respectively. Results show the application of the TPS can help the operator increase profit and attract more users. The loyal users are assigned the highest price, while they still contribute the most to the operator’s profit with the highest number of carsharing trips. The losing users and potential users are comparable in terms of the number of trips, while the latter still makes more profit.

Multiple operators commonly coexist in one-way carsharing systems. Therefore, the performance of the system is worth exploring. We used one-way carsharing systems with two operators as an example, assuming that one joins first and is called the leader, and another is named the follower. A nonlinear mixed-integer bilevel programming model is set to jointly optimize the allocations (including the number of shared cars and parking spaces) and the relocations. The users’ preferences are included by comprehensively considering the travel cost, number of available shared cars at the departing station, and the number of parking spaces at the arrival station. Relocations are also performed in the upper-level model and the lower-level model to maximize the profits of the leader and the follower, respectively. The models of both levels connect by setting the number of parking spaces at each station and the users’ choice between operators. A customized adaptive genetic algorithm is proposed based on the characteristic of the model. Case studies in Beijing reveal that, compared to a single-operator carsharing system, the total profit and demand satisfied by shared cars increased significantly in two-operator carsharing systems, with increases of 37.59% and 56.55%, respectively. Considering the users’ preferences, the leader can meet 266.84% more demands and earn a 174.76% higher profit. As for the follower, the corresponding growth rates are 124.98% and 36.30%, respectively.

No abstract