Freight Electric Vehicle Charging Demand Forecasting Using GPS Data by Developing Geographic PageRank Model

This paper addresses the accurate prediction of charging demand for freight electric vehicles (FEVs) using GPS data, crucial for optimizing charging infrastructure planning. We introduce the Developing Geographic PageRank (DGPR) model to improve prediction accuracy. DGPR utilizes GPS data to process origin-destination (OD) information, considering directional factors and social dimensions like points of interest (POI) and land use. It divides the study area into grid cells, creating a directed graph from OD data, and employs the PageRank algorithm to determine charging demand intensity for each area. The results underscore the DGPR model’s significant impact on precise FEV charging demand prediction. This research facilitates efficient charging infrastructure planning, promoting the sustainable development and effective operation of freight electric vehicle transportation systems.