07-18, 14:30–14:35 (Europe/Sarajevo), PA01
The transition towards more sustainable transport together with a worldwide push for decarbonization promotes the adoption of light-duty electric vehicles (EVs). Nevertheless, for EVs to run on par with or better than internal combustion engine vehicles, they require convenient enough charging infrastructure (Knez et al., 2019). EV charging infrastructure must accommodate shifting demands in terms of density (queuing), frequency (coverage gaps), and dependability (outage) (Hanig et al., 2025). Even if only a small fraction of all car trips are longer than 50 miles (well within the range of today's EVs), long-distance drivers' concerns about charging tend to have a disproportionate effect on their decision to buy a car (Haidar et al., 2022). Moreover, changing stations availability can be critical when choosing a turistic destination.
This research project analyzes the availability of EV charging stations in the Provincia Autonoma di Trento (PAT), a region in the Italian eastern Alps, a popular touristic destination for Italians and northern Europeans.
While an Italian national repository, PUN, "Piattaforma Unica Nazionale dei punti di ricarica per i veicoli elettrici" of the Ministero dell'Ambiente e della Sicurezza Energetica (Single National Platform for Charging Points for Electric Vehicles of the Italian Ministry of Environment and Energy Security) is available for consultation, its dataset cannot be downloaded as a map or a table for processing. Therefore the Open Charge Map dataset, available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) license, has been used. While this charging points database is far from complete, it is fairly representative of the distribution and density of the charging stations. The JSON dataset for Italy has been converted to CSV and the points within the Provincia Autonoma di Trento have been extracted.
The road network has been provided by the local government, Provincia Autonoma di Trento, with a 1:10000 scale, again under the CC-BY 4.0 license. Only the paved roads have been used.
The road network and the charging stations have been combined, placing a node in each station, at the each road intersection and on each road extremity.
With this configuration, the distance of each road to the closest charging station, defined as the minimum distance of the starting or ending node of the arc representing the road, has been evaluated: the minimum distance is below 1 km for most of the roads, with only a few roads above 7 km.
To provide a better representation of the distance between charging stations and potential users a set of points has been created along the roads with a distance of 500m. The distance to charging points has been evaluated for these 8975 points. Nodes belonging to roads shorter than 100m have been removed because they would have too mach influence on the distance distribution.
The mean distance from the charging points is 4749.4 m, with a standard deviation of 4592.6 m. The maximum distance of 36766.6 m, and, as expected the minimum is 100 m. Only 3161 (35.22%) points have a distance above 5 km and 1104 (12.30 %) above 10 km.
To analyze the distribution of the charging stations their density has been evaluated by extracting the charging points for each municipality. The province has 166 municipalities, ranging from relatively large cities in the main valleys to very small municipalities in secondary valleys.
The number of charging stations per municipality is quite low, 1.9 on average, but 72 (43.4%) municipalities have no charging points at all. For the other 94 (56.6%) municipalities which do have at least one charging station, the average number is of 3.32 charging point per municipality, with a standard deviation of 3.79.
Results are compatible with a recent Italian national report (MOTUS-E, 2025) indicating that more than 40% of the municipalities have no charging stations. Moreover, around 30% of Italy has a distance to the nearest charging station above 5 km, 6% above 10 km. However, results are not really comparable because the national report does not employ network analysis but a coarse raster analysis with 1 km resolution and, more importantly, it takes advantage of the access to a more complete charging stations dataset.
Future developments include the repetition of the analysis for other Italian regions, the differentiation of the analysis per types of EV chargers and the use of a more comprehensive charging stations dataset. The availability of traffic data is being investigated since it would make it possible to verify whether the charging stations distribution match the traffic distribution or it is possible to optimize its configuration to serve the largest number of vehicles.
The main limitations of the analysis come not from the processing tools but from the insufficient availability of data, which are often in fragmented, proprietary and inaccessible datasets.
All analyses and statistical and spatial processing were carried out using only FOSS, demonstrating the power and versatility of these software tools. In particular, topological analysis has been implemented using python with numpy and geopandas for data processing and igraph for network analysis. The Matplotlib library has been used for data visualization. QGIS has been used for coordinate conversion, map representation, table processing and geoprocessing.
Hanig, L., Ledna, C., Nock, D. et al. Finding gaps in the national electric vehicle charging station coverage of the United States. Nat Commun 16, 561 (2025). https://doi.org/10.1038/s41467-024-55696-8.
MOTUS-E, Le infrastrutture di ricarica a uso pubblico in Italia, (2025), https://www.motus-e.org/studi_e_ricerche/le-infrastrutture-di-ricarica-a-uso-pubblico-in-italiasesta-edizione/ (visited on 09/03/2025).
Knez, M., Zevnik, G. & Obrecht, M. A review of available chargers for electric vehicles: United States of America, European Union, and Asia. Renew. Sustain. Energy Rev. 109, 284–293 (2019), https://doi.org/10.1016/j.rser.2019.04.013.
Haidar B., Aguilar Rojas M.T., The relationship between public charging infrastructure deployment and other socio-economic factors and electric vehicle adoption in France, Research in Transportation Economics, 95, (2022), https://doi.org/10.1016/j.retrec.2022.101208.
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Paolo Zatelli is born in Pavia on March the 2nd 1968. He obtained the diploma at A. Roiti High School
of Ferrara in July 1987. He graduated in Environmental and Land Engineering at the University of Trento cum laude in 1994.
He obtained a PhD degree in Topographic and Geodetic Science with the thesis "Application of
wavelets in geodesy" at the Polytechnic of Milan in 1998.
From February 2001 to 2016 he is researcher and from 2016 associate professor of Geomatics at the Department of Civil, Environmental and Mechanical Engineering of the University
of Trento, Italy.
The didactic activity regards Survey, Survey and statistics, Photogrammetry, Numerical cartography
and GIS, Remote sensing and GIS, Mathematical and statistical methods. He is coordinator and lecturer
for the course "Environmental data management and analysis with GIS", for the Doctoral School in
Civil, Environmental and Mechanical Engineering of the University of Trento.
He organizes and teaches in several courses for GIS professionals, such as "Theorical and practical
course on GRASS, Free and Open Source GIS and GEODATABASE", "GIS theory and applications"
and "GPS, from theory to applications", and masters, such as the second level Master "Analysis and
management of Geotechnical systems -SIGEO".
The research activity regards different aspects of the land survey and of treatment of related
information, both metric and semantic, including therefore modern data acquisition, efficient
elaboration and integrated management techniques. The study of modern survey methods includes both point positioning techniques, such as GPS, and global methods such as laser scanning, remote sensing and digital photogrammetry. Further researches include the study of high resolution earth gravity field determination by satellite geodesy. Methods for multiresolution data analysis for efficient data representation and filtering have been developed.
Geographic data treatment and management have been studied with the use of GIS, where new
functionalities have been implemented for the creation of environmental models.
He has acted as local and national research project coordinator, reviewer for international and national
journals. Ha has organized international and national meetings and he has been part of the committees of several conferences, for which he has organized many workshops.