# A Tailored Pricing Strategy for Different Types of Users in Hybrid Carsharing Systems
## 5 Conclusions  
In hybrid one-way carsharing systems, a tailored pricing strategy (TPS) for different types of users is proposed to maximize the operator's profit and minimize all users' costs simultaneously. Autonomous vehicles and conventional sharing cars are incorporated into hybrid carsharing systems. Users can be clustered into three types based on the operational data by the K-means algorithm. They are the loyal users, losing users, and potential users, respectively. A bi-objective nonlinear programming model is established and then transformed to a single-objective model by weighting each objective function. A genetic algorithm is applied to solve it. In order to testify to the efficiency of the TPS, case studies in Lanzhou, China, are performed. The main contributions of this study can be listed below:  
1. Considering that not all demands can be satisfied by the hybrid carsharing system. A penalty coefficient is defined to reflect the potential effect of unsatisfied demands for each cluster on the profit. It is incorporated into the objective function. It combines travel potential, consumption potential, travel efficiency, and average revenue. The penalty coefficient for loyal users is the highest. It shows the importance of loyal users on the profit. 2. Differential prices are offered to different users by considering the unique characteristic of each user cluster. The price elasticity combines the price variations and demand variations, and it is used to distinguish different clusters. The carsharing operator can get 14.48% more profit at less extra costs. The loyal users contribute the most to the carsharing operator's profit and the number of trips, while they pay the highest prices, especially during peak hours. The numbers of carsharing trips for the other two clusters are nearly the same. Compared with the losing users, the potential users can make more profit. The carsharing operator should pay the most attention to loyal users, then the potential users. 3. Compared with the carsharing system only with conventional cars, the performance of the hybrid carsharing system is explored. It reveals that the adoption of the SAVs benefits the operator and the users, especially the loyal users. Therefore, the conventional carsharing operator should embrace the new technology (referring to AVs in this study). These results can be the potential directions for the carsharing operator. For example, attaching great importance to the operational data and then analyzing the characteristics of users. Based on the details of the users in the system, the operator can provide many personalized services at different prices; not only more users can be attracted, and more loyal users there would be, but also higher profit and service levels. There are still many details worthy of discussion. Due to the limitations of the operational data, indicators like \"walking cost\" and \"waiting cost per minute\" for all user clusters are assumed to be the same. For simplicity, there are some restrictions on sharing autonomous vehicles like \"one sharing autonomous vehicle can only serve one user per trip\" and \"it needs to return to carsharing stations when a trip is finished\". To further make the model more realistic, the characteristics of users should be explored deeply. Moreover, one sharing autonomous vehicle can pick up more users per trip, and a corresponding price mechanism should be given in the future. Further, there are many competitors for sharing cars, i.e., ride-sharing, taxis, etc. Various traffic modes should be considered to compete with sharing cars. Conceptualization, R.L. ; data curation, X.Z. and Y.W. ; formal analysis, Y.W. ; funding acquisition, X.Z. ; methodology, Y.W. ; project administration, X.Z. ; resources, X.Z. ; validation, Y.W. ; visualization, R.L. ; writing--original draft, R.L. ; writing--review & editing, R.L. and X.Z. All authors have read and agreed to the published version of the manuscript.