# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 3 Data Acquisition
### Data Categories  
Spatial- and time-dependent data can be categorized depending on the system they are describing:  
* Transport network data: characterizes different aspects of transport infrastructures depending on the studies' application. This can include parking areas, existing gas stations, public transport stations, etc. For this purpose, GIS data are used to map major intersections and road segments as in [38], patrol stations as in [66], as well as motorway exits [26] and highway junctions [29].
* Socio-economic data: including the type of neighborhood to be served (industrial, residential, etc.), GDP, EV ownership, etc.
* Electric system data: including geo-spatial data of the electric power system, the characteristics of the substations and the cablings, and the parameters that characterize the operations of the system. Modelling of the power system is implemented to assess the impacts of the charging demand on the distribution system. Real networks are considered in few cases [108], whereas the majority of cases implement benchmark [109; 110] or synthesized grids [111].
* Traffic data: extracted from GPS data to register the time of vehicles' successive positions or from stations that assess passing vehicles through a particular point of measurement. Origin-destination (O-D) data can also be used effectively when coupled with timestamps or even activity-based labelling of trips. Traffic data are then analyzed to assess the mobility charging demand and ultimately the charging behaviour of users; this information is processed to extract the paths followed by vehicles, as in [34; 52; 112], or parking times [113]. In their review study, the authors in [114] differentiate between three categories of EV driving and charging data: surveys (such as the \"Mobilitat in Deutschland 2017\", used in the recent study [115]), EVs trials, and charger trials. Charging behaviour can be analyzed from each of these sources, but a combination of two or all the categories results in the most accurate understanding of the phenomenon. Although GPS data were largely used in the reviewed literature, these input data can insufficiently represent complete mobility patterns of a population. Therefore, particular attention must be applied when using samples or creating new databases for studies. Finally, travel demand can be modelled through one-day or multi-day data, the latter being more accurate for reproducing travel needs [107], given the differences in mobility patterns between weekdays, weekends, seasonal variations, and festivities. A small number of articles use accurate simulations to calculate the charging demand of EVs. In particular, the authors of [57] implement a discrete simulation event tool in MATLAB(r) in order to accurately estimate the energy consumption and downtime needed to charge and complete trips for a fleet of electric buses, taking into account stochastic events with a high level of precision (e.g., traffic lights). Instead, study [38] assesses the queuing probability through simulation in order to size the CI and ensure a contained queuing risk.