Solving the location-allocation problem of charging stations for electric vehicles on maps using machine learning

Abstract

In this article, we explore the challenge of strategically placing electric vehicle (EV) charging stations to bolster the growth and infrastructure of electric transportation. With the increasing adoption of EVs, ensuring accessible and convenient charging options is crucial. We have developed a unique program that leverages machine learning techniques to analyze geographic maps for identifying prime locations for charging station installation. This program evaluates various critical factors such as population density, transportation routes, existing infrastructure, and others. By incorporating these elements, our approach aims to establish a more effective and accessible charging station network, enhancing both environmental sustainability and user convenience. This research holds significant practical value, offering essential insights for urban planners, EV infrastructure investors, and policymakers working towards a greener transportation future. The program serves as a decision-making tool for strategically placing charging stations, addressing both present and future urban and residential needs. The article provides a detailed description of the collection of necessary information, the algorithm for training a neural model based on this information and presents the results of applying the algorithm to the map of Moscow. These findings underscore the potential of machine learning in refining urban infrastructure and promoting sustainable city development.