# An Overview of Shared Mobility Operational Models in Europe
## 3 Carsharing
### Conducted Research  
Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service was published by [49], in which they developed an optimization model specifically for the round-trip carsharing fleet placement problem, termed the Fleet Placement Problem (FPP). The model uses two key parameters--walking distance and maximum user coverage--to establish a balance between accessibility and coverage of the service. Applied to three real-world instances (two in Luxembourg and one in Munich), this model marked the first use of exact real-world data for this type of problem, adding notable relevance. Their research demonstrated that NSGA-II notably outperforms manual allocation in terms of both user coverage and the approximated Pareto front, providing decision-makers with a range of solutions. This paper also suggests future applications of the model in cities like London, Athens, and Paris. After a carsharing service launch, further objectives, such as fleet utilization, fleet size, and station count, may become relevant, and the authors recommend a custom metaheuristic algorithm tailored for the fleet management problem. In a related paper, [49] the authors further developed their approach, focusing on the initial placement of a car fleet for round-trip carsharing services using a multi-objective optimization model. This study, which also employs metaheuristic algorithms, emphasizes the novelty of using real street networks for optimization, a method that has not been previously explored in the literature. Their model sought to determine optimal fleet vehicle locations based on three primary objectives: maximizing the number of citizens who can access a vehicle within a predefined walking distance, minimizing the distance to public transport, and optimizing or maintaining the desired fleet size. This model was tested using real-world instances in Munich and Hamburg. Future work for this study will involve expanding the Vehicle Placement Problem to integrate dynamic data, such as user demand and vehicle movement, and enhancing model realism by adjusting user distribution according to building sizes [50]. Looking at another approach, [51] aimed to optimize the strategic design of mixed carsharing systems that combine free-floating and station-based models. The main objective was to leverage the advantages of both systems while minimizing their drawbacks,contributing to sustainable urban mobility. The study used a continuous approximation (CA) model to optimize the key decision variables: fleet size, station density, and rebalancing intensity. It evaluated both the operational agency costs and user access and service reliability costs. Then the model was applied to a case study in Barcelona, Spain, using real-world parameters and scenarios to test its performance and robustness. Their results showed that mixed systems performed better than standalone configurations, where with mixed systems around 80% of potential demand was served, while with pure free-floating or station-based systems only 70% was served. It was also concluded that a higher station density leads to greater demand coverage and better system balance. Through this research, some key trade-offs emerged: optimal designs reduced fleet sizes while increasing repositioning operations; operative costs were dominant among agency costs (between 60% and 65%); and mixed systems achieved profitability with reduced idle times and a higher utilization rate (between 13 and 29 trips/vehicle/day). A summary of the research conducted and future research proposals is presented in Table 2.  
The evolution of carsharing's has shown that it may minimize car ownership, maximize urban mobility, and encourage sustainable travel choices. Carsharing is a major part of urban transportation; however, it is only one aspect of a larger trend toward shared mobility solutions. In addition to carsharing, bikesharing has grown into a very suitable and sustainable option, especially for short urban trips.