TY - JOUR
T1 - GIS-Based Geospatial Analysis for Identifying Optimal Locations of Residential On-Street Electric Vehicle Charging Points in Birmingham, UK
AU - Kazempour, Milad
AU - Sabboubeh, Heba
AU - Pirouz Moftakhari, Kamyar
AU - Najafi, Reza
AU - Fusco, Gaetano
N1 - Copyright © 2024 Published by Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2024/11/28
Y1 - 2024/11/28
N2 - Global urbanization and the growing need for sustainable transportation solutions are increasing the demand for electric vehicle (EV) infrastructure. This research aims to identify optimal locations for Residential On-Street Electric Vehicle Charging Points (RO-EVCPs) that are essential for residents without access to off-street parking and to support the transition to a sustainable urban environment in Birmingham. A GIS-based model, incorporating key location criteria such as accessibility, environmental impact, and infrastructure compatibility, can effectively identify suitable locations for RO-EVCP deployment, improving access and inclusivity for electric mobility. The study develops a customized geographic information systems (GIS) model, utilizing the Analytic Hierarchy Process (AHP) for weighting location criteria, with validation through geospatial tools like Google Earth® and Street View. The model generates a spatial suitability map, categorizing areas into optimal, moderate, and limited suitability for EV charging, with an emphasis on accessibility, environmental impact, and inclusiveness. High-priority streets and recommended charging point numbers are identified. The findings emphasize accessibility and inclusiveness, crucial for individuals without off-street parking, promoting an equitable transition to electric mobility. This research contributes to sustainable urban mobility planning by advocating data-driven decision-making in EV infrastructure development, aligning with climate change mitigation objectives.
AB - Global urbanization and the growing need for sustainable transportation solutions are increasing the demand for electric vehicle (EV) infrastructure. This research aims to identify optimal locations for Residential On-Street Electric Vehicle Charging Points (RO-EVCPs) that are essential for residents without access to off-street parking and to support the transition to a sustainable urban environment in Birmingham. A GIS-based model, incorporating key location criteria such as accessibility, environmental impact, and infrastructure compatibility, can effectively identify suitable locations for RO-EVCP deployment, improving access and inclusivity for electric mobility. The study develops a customized geographic information systems (GIS) model, utilizing the Analytic Hierarchy Process (AHP) for weighting location criteria, with validation through geospatial tools like Google Earth® and Street View. The model generates a spatial suitability map, categorizing areas into optimal, moderate, and limited suitability for EV charging, with an emphasis on accessibility, environmental impact, and inclusiveness. High-priority streets and recommended charging point numbers are identified. The findings emphasize accessibility and inclusiveness, crucial for individuals without off-street parking, promoting an equitable transition to electric mobility. This research contributes to sustainable urban mobility planning by advocating data-driven decision-making in EV infrastructure development, aligning with climate change mitigation objectives.
UR - https://www.sciencedirect.com/science/article/pii/S2210670724008126?via%3Dihub
U2 - 10.1016/j.scs.2024.105988
DO - 10.1016/j.scs.2024.105988
M3 - Article
SN - 2210-6707
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
ER -