## 2 Literature Review
### Methods Used for the Location of Charging Infrastructure for Private Passenger Vehicles  
Moving from a fleet of fossil-fuel-based vehicles to a fleet of zero-emission vehicles in urban and non-urban contexts could foster a more sustainable future. Electric vehicles (EVs) should therefore be given priority so that they can gradually replace conventional vehicles. In this framework, it is essential that the charging infrastructure can support EVs and ensure the overall functionality of the system [24]. A well-planned network of charging stations (CSs) is a key element for the diffusion and utilization of EVs. Mirroring this importance, several scholarly studies focus on the analysis of the development of charging infrastructures. Scholars investigate the problem of finding suitable locations for public EV charging stations from different points of view. On the one hand, the issue is addressed only as a mathematical problem. For instance, a multi-agent system (genetic algorithm using a utility function) is developed by [25] to analyze the potential location for EV charging stations. Their method characterizes the areas where future CSs could potentially be located, integrating information from various data sources (i.e., traffic, social networks, population, etc.) as a baseline. The issue of the distribution of EV charging stations is mainly an allocation problem, influenced by several criteria [24], which are likely in conflict and constrained by different variables (space, cost, coverage, energy availability, etc.). There are studies that apply mathematical methods and optimization models to solve this problem, whereas some research focuses on CP location analyses that are also based on multi-criteria and/or Geographic Information System (GIS)-based methods. The authors of [26] analyzed the associations between information on points of interest (POI) at a large urban scale and existing CP locations, exploiting the potential of big data. Based on this analysis, they calibrated the spatial distribution of EV charging demand. Applying a simple linear regression model that associates the use of existing CPs with the density of different POI categories, the authors selected the most statistically significant POI categories for what concerns the influence on EV charging demand. Reference [27] applied a geospatial analysis for the allocation of charging infrastructure for EVs within a city (positioning CSs along the urban road network) and a region (considering rural roads and motorways), relying on open-source GIS tools. Their methodology provides the optimal locations of EV charging stations for both types of networks, identifying areas with a high potential for CS installation. The combination of spatial statistics and a maximum-coverage location model is performed in the approach developed by [28] for the optimization of the placement of EV charging points to maximize the demand coverage. The method identified possible drivers for CP locations, highlighting the potential spatial diversity and the dependency effect among drivers. The authors of [29] focused on two other relevant aspects: CO\({}_{2}\) emissions due to the round trips to CSs and constraints for the charging due to the remaining electricity. According to the authors, these two features can influence drivers' location choices and charging decisions. In this study, an optimization approach is proposed to address the location issue, investigating the impact of travel data (GPS-enabled trajectory data) in different periods on CS location solutions. The authors of [30] elaborated a method to deploy charging stations and meet both the inter- and intra-city charging demand, considering existing and future charging installations. The developed method is based on a weighted multi-criteria analysis of roads that identifies several variables (demographic, economic, environmental, and transportation-related), as well as the available POIs that influence the potential use of charging stations.