Interpretive structural model for influential factors in electric vehicle charging station location

Electric vehicles (EVs) are emerging as a pivotal solution for achieving sustainable and eco-friendly transportation. Integrating EVs into transport networks is crucial for fostering environmentally sustainable growth in smart cities, addressing carbon emissions, and reducing reliance on fossil fuels. With the increasing popularity of EVs, the demand for charging stations proliferates. However, selecting optimal locations for electric vehicle charging stations (EVCS) is a complex task that requires careful consideration of various factors. Existing studies have not adequately addressed these factors' intricate relationships and interdependencies. This study aims to fill this gap by employing interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to analyze the influence factors and interactions between them. Based on a comprehensive literature review and expert input, we identify 18 key factors that influence EVCS. The result shows that proximity to EV drivers, strategic placement, integration with daily routines, timely availability, and EV ownership rates are the most influential and objective considerations. The established integrated structured model provides a valuable tool for understanding and optimizing the complex relationships among the identified factors, aiding in informed decision-making for EV charging station locations.