# A Tailored Pricing Strategy for Different Types of Users in Hybrid Carsharing Systems
## 2 Literature Review
### SAVs  
Research related to SAVs mainly focused on analyzing the advantages of the SAVs, the assignment and/or dispatch problem, and the price setting. The most common advantages of AVs are to reduce the ownership of private cars [23], less waiting time, shorter travel mileage [24; 25], and fewer empty trips [26].  
The assignment problem of SAVs is also widely studied. [PERSON] et al. [27] matched AVs to users' demands and found that the waiting time and the number of unoccupied trips significantly decreased. Users were clustered by considering the similarity of time and space; then, SAVs were assigned to them. Several users in the same cluster can share an SAV in the study. Extending the waiting time would decrease the fleet size [28]. The joint optimization problem of the assignment and dispatch of AVs was also studied [19]. Centralized dispatchers were designed for immediate travel requests, and decentralized dispatchers served reservation requests. The dispatch strategy worked well in improving the revenue and satisfaction rate of the demands.  
Researchers attached much attention to the price-setting problem for AVs. [PERSON] et al. [29] explored the advantages of dynamic pricing in a hybrid system with public transport and AVs. A hybrid service of private and ride-sharing was provided by AVs. They found that dynamic pricing was much superior to static pricing in increasing the number of shared trips. By considering the distance, parking fee, traffic, and road conditions, the price for long-range autonomous valet parking was optimized [17]. The performance of hybrid autonomy systems with AVs and human-driven cars was also explored [30]. By setting differentiated prices for different types of cars, the equilibria of the system were achieved efficiently. AVs were assumed to use to provide E-hailing services. A link-based dynamic pricing model was proposed to improve the performance of the road network system [31]. In a hybrid ride-sharing system with AVs and human-driven cars coexisting, the connection between the fleet size of AVs and the operational costs was studied by adjusting the prices for riders and the compensation cost for drivers. It turned out that the operational costs had a significant impact on the setting of the fleet size of AVs. AVs were adopted by Uber and Lyft (examples of ride-sharing companies). [PERSON] [32] revealed that ride-sharing is one of the competitors of sharing cars. [PERSON] [33] studied the ride-sharing system from the perspective of drivers considering Uber and Lyft compete with each other. Results showed that the operational cost played an essential role in the drivers' job satisfaction. Furthermore, [PERSON] et al. (2018) pointed out that reward programs could be used to keep the users as loyal customers.  
In previous studies, AVs were mainly used in the taxi or ride-sharing systems. There are no studies focused on hybrid carsharing systems with both the service of SAVs and the service of CSCs. Further, uniform prices were offered to users, and rare research about the differentiated prices for hybrid carsharing systems. To fill the gap, the choice behaviors of different user clusters are analyzed in hybrid carsharing systems. Then, a TPS is proposed to set tailored prices for different user clusters by considering the characteristics of each user cluster, and a bi-objective nonlinear programming model is formulated to maximize the operator's profit and simultaneously minimize all users' costs. The optimal price, fleet size, location, and size of SAV stations are jointly determined.