GIS-based geospatial analysis for identifying optimal locations of residential on-street electric vehicle charging points in Birmingham, UK

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 (R) 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.