# Carsharing Vehicle Fleet Selection from the Frequent User's Point of View
## 1 Introduction  
Carsharing services systems, i.e., short-term automated car rentals, are solutions that are gaining popularity in the world. The solutions owe their intensive development, among others, to their high convenience and self-commission of use [1]. What is more, the systems also benefit from the fact that users do not have to bear the costs associated with vehicle maintenance, relatively low usage prices, and the possibility of free parking in urban areas or the use of dedicated parking spaces. The advantages of using the systems also translate into favorable market development statistics. The size of the carsharing market in 2020 exceeded USD 2 billion [2]. On the contrary, it is expected to increase by the end of 2022 with a Compound Annual Growth Rate (CAGR) of 17.4% [3]. Along with the intensification of carsharing services in global markets, many improvements have been made in terms of management and operation. These improvements were related, among others, to the issues of appropriate adjustment of services to the public space and the conditions of a given city, optimization of the functioning of systems, or adaptation of systems to changing regulations or conditions, for example, during the COVID-19 pandemic [4, 5, 6, 7, 8]. Although many issues have been improved, there are aspects that carsharing users classified as problematic. Among the many different complaints, there are issues regarding the carsharing fleet [9]. While in the literature review one can find a wide range of issues related to the modeling and optimization of systems [8], the issue of the vehicle fleet used in carsharing systems is usually considered similarly. The first fleet trend that researchers are focusing on is the carsharing fleet size. For example, the research of [PERSON] et al. addressed the tactical electric vehicle fleet size problem faced by carsharing service providers, while considering the operational vehicle assignment, vehicle relocation, and vehicle charging strategies in pursuit of profit maximization [10]. [PERSON] et al. presented a method for determining the deployment of one-way electric carsharing services within a designated region that maximized the total profit of the operator [11]. In turn, [PERSON] and [PERSON] focused on a dynamic carsharing decision support system, pointing out that fleet size in carsharing services is dependent on the pattern of user requests [12]. The second trend identified in the literature related to the fleet of carsharing is related to the location and relocation of vehicles. For example, [PERSON] and [PERSON] focused on a feature-based selection of carsharing relocation modes, identifying the best modes for car movement in the city [13]. [PERSON] and [PERSON] in their study focused on free-floating carsharing and explored the trade-offs between fleet size and hired vehicle redistributors, intending to maximize the demand level that could be satisfactorily served [14]. In turn, [PERSON] et al. proposed a mathematical programming-oriented approach and introduced a simple linear model based on integer flow variables [15]. Their solution was based on three optimization criteria: maximizing satisfied carsharing demands, while minimizing the fleet of vehicles and the relocation operations [15]. The third trend is research on the environmental impact of the carsharing fleet in cities and the surrounding area. For example, [PERSON] and [PERSON] looked at the environmental impact of the electrification of the carsharing fleet [16]. [PERSON] and [PERSON] analyzed the life cycle of the carsharing fleet [17]. [PERSON] et al. analyzed the CO\({}_{2}\) emissions of the vehicle fleet [18]. While [PERSON] et al. estimated the environmental benefits related to carsharing [19]. Their research has shown that there are benefits deriving from the use of carsharing in terms of reducing emissions of pollutants: there is a reduction of 25% for PM10 and 38% for CO\({}_{2}\)[19].