Optimizing Electric Vehicle Charging Station Locations Based on Urban Road Network Congestion Tracing

Exhaust emissions from congested road segments constitute a significant source of urban air pollution. Resolving traffic congestion throughout the road network presents considerable challenges. However, alleviating tailpipe emissions on congested roads can be achieved by increasing the proportion of electric vehicles (EVs) in the traffic flow. Therefore, we propose a method for optimizing the layout of EV charging stations based on urban road networks congestion tracing. This method traces congestion sources through similarity between road networks, and evaluates the installation potential value of adjacent candidate installation points using the congestion contribution degree of the road segment as an indicator. The analysis is conducted on 100 routes within the Qinhuai district of Nanjing city, using spatiotemporal similarity metrics. The utilization of point-of-interest and traffic data from online mapping sources overcomes the complexity of road network structure and the sparsity of data collection, making it suitable for large-scale road network research. Clustering the routes using the SimpleHerm algorithm reveals three meaningful clusters, indicating that the Qinhuai district predominantly consists of three main longitudinal routes with lateral road segments converging towards them. By using similarity measures to track congestion, four congestion originating routes were identified. Thus determine the installation potential value of candidate charging stations. The study findings indicate that while Qinhuai district currently possesses an adequate number of EV charging stations to meet basic demands, only 20% of these stations effectively guide EV traffic proportions on congested road segments.