A simulation model for locating electric vehicle charging stations (Case Study: Kerman city)

Document Type : Original Article

Authors
Borna Aghababaei 1
Amir Reza Mamdoohi 2
1 M.Sc., Graduate, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
2 Associate Professor, Faculty of Civil & Environmental Eng., Tarbiat Modares University, Tehran, Iran; & Adjunct Professor, Department of Civil, Geological & Mining Eng., Polytechnique Montreal, Canada.
10.22034/road.2026.573652.2471
Abstract
The growing adoption of electric vehicles has turned charging infrastructure planning into a key challenge for urban transportation systems. This study aims to identify optimal locations for electric vehicle charging stations and evaluate their operational performance in the urban network of Kerman using an integrated spatial–operational framework. The methodology combines spatial analysis in a Geographic Information System environment with microscopic traffic simulation. The study population includes the road network of Kerman and all daily urban trips. In the spatial phase, 1,903 trip-attracting points extracted from the OpenStreetMap database were classified into 11 major land-use categories. In the operational phase, 13,500 electric vehicle trips were simulated as a representative sample of the city’s 782,181 daily trips, based on the urban road network and the origin–destination matrix. Data analysis involved the Analytic Hierarchy Process to weight location criteria, generate suitability maps, select high-potential sites under different scenarios, and simulate charging station performance in the SUMO environment. Results show that scenarios based on AHP weighting and site selection near fuel stations or parking facilities achieve a balanced spatial distribution while keeping increases in travel time and travel distance within acceptable limits. Sensitivity analysis further indicates that increasing charging power and the number of charging ports significantly reduces waiting times and improves overall network performance. These findings highlight the importance of combining accurate spatial planning with technical optimization to support the sustainable development of urban charging infrastructure.
Keywords
EV charging station location
electric vehicles
Geographic Information System (GIS)
Analytic Hierarchy Process (AHP)
Simulation of Urban Mobility(SUMO)
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