# A Tailored Pricing Strategy for Different Types of Users in Hybrid Carsharing Systems  
[PERSON]  
1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; [EMAIL_ADDRESS] (R.L. ); [EMAIL_ADDRESS]. e.g., incentive, discount/coupon, or penalty. Different pricing strategies have different influences on users' behaviors [11]. Further, different types of users have different sensitivity to the same price [12] and other distinct characteristics, such as risk preference [12], trip frequency [13], trip purpose [14], and activity persistency [13; 15]. Most focus on determining the optimal prices for a whole day or different prices in different periods. However, prices are still uniform for all travelers, and the uniform pricing strategy cannot precisely meet the demands of different user clusters considering their unique characteristics. Previous studies about the pricing strategies only considered a single service mode in the carsharing system, i.e., the service of CSCs [16] or SAVs [17]. For the service of CSCs, the operator only relocates CSCs between stations to provide station-to-station services to users by maximizing the profit by it containing the cost of relocations, gas consumption, and depreciation and maintenance costs for sharing cars and parking spaces [18]. While for the service of SAVs, the operator does not need to perform relocations, and SAVs are assigned to serve users from their origins to destinations by maximizing profit concerning the assignment cost of SAVs, gas consumption, depreciation, and maintenance costs [19]. In the hybrid carsharing system with both the service of CSCs and the service of SAVs, users can choose different services based on some rules. Therefore, users' mode choices should be considered in hybrid carsharing systems, which are different from the studies in carsharing systems with a single mode. In this study, we consider a hybrid carsharing system with both the service of CSCs and the service of SAVs. Users' choice behaviors are taken into account based on the utilities of each mode. For the service of CSCs, the utility is related to the walking cost from origins to borrowing-car stations, driving cost between stations, and the walking cost from returning-car stations to destinations. For the service of SAVs, the utility is related to the cost of waiting for the SAVs to drive from the nearest autonomous sharing stations to origins and the rental cost of SAVs between origins and destinations. Then, a tailored pricing strategy (TPS) for different user clusters is proposed by combining user clusters' characteristics in the hybrid carsharing system. Finally, a bi-objective nonlinear programming model is built by considering the operator's profit and the travel costs of all users simultaneously to determine the optimal fleet size, location, and size of autonomous carsharing stations. Case studies are carried out to explore how the TPS impacts the carsharing system. It turns out that the TPS can reduce all users' costs and increase the operator's profit at the same time. The main contributions of this study are given below:  
* In a hybrid carsharing system with both the service of CSCs and the service of SAVs, users' choice behaviors for different service modes are modeled by considering the rental costs and walking costs of the two service modes. * Based on the different characteristics of each user cluster, a bi-objective nonlinear programming model is built up to determine the optimal fleet size and prices for different service modes. The TPS is proposed to improve the operator's profit and the system's service level. Results show the proposed TPS can enhance the operator's profit and lower users' costs. The paper is organized as follows. In Section 2, literature about SAVs and pricing strategies is reviewed. Then, in Section 3, considering the characteristics of each user cluster, a bi-objective nonlinear programming model is formulated to set tailored prices for different user clusters in the hybrid carsharing system.