## 1 Introduction
Another approach followed in the scientific literature is to include survey or similar data to define the charging infrastructure usage. Using survey data and basic assumptions regarding parking behavior and fleet composition, [PERSON] et al. [27] employed an agent-based simulation to estimate the number of charging points (CPs) required in Germany. The results indicate significant impacts from on-street charging, home charging, and the proportion of plug-in hybrid electric vehicles (PHEVs) in the fleet on the number of necessary CPs. However, no information is provided on the impact of charging points on the network, considering the increase in electricity demand overall or the hourly distribution of charges. A more comprehensive analysis is provided by a similar study [28], which also used an agent-based simulation, incorporating charging behavior and location choice for charging as explicit inputs based on realistic assumptions. The needed number of charging points and their daily use was calculated for an urban case study. Other studies used instead a different methodology to evaluate the charging infrastructure usage. [PERSON] et al. [29] analyzed the operation of a charging point in combination with a photovoltaic system in Indonesia using the HOMER framework. However, the case study relates to a single recharging point in an urban context, as the geographical variability in both photovoltaic production and electric vehicle recharging demand cannot be considered. In a similar study [30], the case study is expanded to include these aspects. In this case, however, a limitation is to have considered an idealized charging station where there is a perfect distribution of vehicle charging, and all the charging points are always used at any time of the day. In another study, [PERSON] et al. [31] considered the operation of various charging points, within an analysis of California's energy system. In this case, the vehicle charging profiles are not based on real data but on dumb or perfectly smart charging assumptions, where the vehicle charging is arbitrarily set afterwards to fill the valleys created by the already existing load. Although this approach may be considered sufficient for the purposes of the study, it does not allow consideration of the different energy demands of charging, depending on the geographical location of the points considered, such as the difference between tourist and non-tourist areas. The present study aims to provide a comprehensive analysis of electric vehicle charging patterns and infrastructure utilization in the Trentino-South Tyrol region of Italy, thus including urban and non-urban areas, by examining high-resolution temporal and spatial data from public charging stations over a three-year period (2021-2023). The innovative aspect of this article lies in the extensive scope of the regional case study, which, unlike other studies in the literature, encompasses a diverse range of spatial and geographical areas and includes various types of charging points, both AC and DC, with different nominal capacities. This breadth allows for the examination of multiple factors influencing the utilization of charging points. The study provides a detailed and varied representation of the charging behavior, including not only common data such as the duration and start time of charging but also crucial data to assess the impact on the network, such as the hourly power demand from each type of charging point. Furthermore, the measured data are compared with data obtained through simulations using the _emobpy_ tool (version 0.6.2, [[https://emobpy.readthedocs.io/en/latest/](https://emobpy.readthedocs.io/en/latest/)]([https://emobpy.readthedocs.io/en/latest/](https://emobpy.readthedocs.io/en/latest/)), accessed on 28 August 2024). Unlike other approaches in the literature, _emobpy_ includes parameters such as daily vehicle movements, which influence the likelihood of finding an available charging point and the usable nominal capacity.