About the data
A spatial analysis was carried out to detect suitable areas for the installation of charging points for private electric vehicles and e-bikes and improve the capillarity of the network as a result.
The first step of the analysis was to partition the territory into cells of equal area and shape. This process, called “tessellation”, is used to compare different data for mapping because it allows one to normalize geographies. In this case, hexagons were used because this minimizes the inevitable errors linked to the normalization of a spatial distribution. In fact, hexagons have the lowest perimeter-to-area ratio and can reduce the sampling bias due to the edge effects of the grid shape. Then, the hexagon grid was filtered by
- selecting the cells intersected by the transportation network (given by municipal, provincial/regional, and national roads in the case of EVs and by cycleways and municipal roads in the case of e-bikes)
- excluding the cells within a certain distance from existing charging points (a buffer of 5 km was considered in the case of EVs and of 2 km in the case of e-bikes).
The remaining cells represent areas with potential infrastructure needs. However, not all of them are suitable for the installation of new charging points. The distribution of infrastructures is usually not homogeneous throughout the territory, but it is strongly correlated with the distribution of Point Of Interests (POIs). In order to determine whether an area is suitable for the installation of the new infrastructures, the most relevant POIs for private electric mobility were included in the analysis, grouped into three categories based on their general function (tourist amenities, public facilities, local infrastructures). Not all POIs have equal influence on the allocation decision. For instance, the existence of hotels might carry more weight when determining the installation of new e-mobility infrastructure.
In order to solve this issue, an expert-driven approach was used. Anevaluation questionnaire was designed and administered to the project MOBSTER partners (6 representatives of the case studies) for the attribution of a subjective score between 1 (minimum) and 5 (maximum) at each POI and at each POI category. The averages of the scores were included in the calculation process to properly weigh the different influences of POIs on the allocation choice:
where: x = average score of POI category
y = average score of POI subcategory
z = number of POI per subcategory
The result consists of two suitability maps, one relating to the needs of electric infrastructures for private electric vehicles and one for e-bikes.
The datasets used in the analysis were the transportation network infrastructures (retrieved from the Autonomous Province of Bolzano geodatabase), the existing charging points for EVs and e-bikes provided, and the selected categories of POIs (both retrieved from the OpenStreetMap geodatabase).
To find out more about the research on electric mobility, please read:
- Sparber, W.; Grotto, A.; Zambelli, P.; Vaccaro, R.; Zubaryeva, A. Evaluation of Different Scenarios to Switch the Whole Regional Bus Fleet of an Italian Alpine Region to Zero-Emission Buses. World Electr. Veh. J. 2023, 14, 91. https://doi.org/10.3390/wevj14040091
- D'Alonzo, V.; Zambelli, P.; Zilio, S.; Zubaryeva, A.; Grotto, A.; Sparber, W. Regional Infrastructure Planning Support Methodology for Public and Private Electrified Transport: A Mountain Case Study. Appl. Sci. 2023, 13, 7181. https://doi.org/10.3390/app13127181
The methodology developed in the study can also be applied in other scenarios. If interested, contact renewable.energy@eurac.edu
Find out more about the project: https://www.progettomobster.eu/
The MOBSTER project is an operation co-funded by the European Union, the European Regional Development Fund, the Italian Government and the Swiss Confederation and Cantons as part of the Interreg V-A Italy-Switzerland Cooperation Programme.