Identifying Hotspots of Electric Logistics Vehicle Charging Demand and Their Determinants Using Spatiotemporal Clustering

The electrification of urban freight is a central pathway for advancing China’s dual-carbon agenda, yet the spatial and temporal mismatch between charging supply and logistics demand remains a major bottleneck. Using Shanghai as a case study, this paper develops an integrated framework of hotspot identification, mechanism interpretation, and planning response for electric logistics vehicle (ELV) charging demand. Based on the operating records of more than 1200 pure electric logistics vehicles in Shanghai from 1 March to 30 November 2023, 85,367 valid charging events were extracted. ST-DBSCAN is used to detect charging demand hotspots, and a negative binomial model is employed to examine their determinants. The results show that charging demand is highly differentiated in space and time, following a pattern of daytime concentration in core logistics areas and nighttime dispersion toward peripheral parking and recharging spaces. Initial state of charge, daily mileage, logistics point of interest (POI) density, and road network density are all significantly associated with hotspot intensity, while the effects of time vary across daytime and nighttime charging contexts. The predominance of slow charging, together with a pronounced midday charging peak (12:00–17:00), points to a potential fast-charging pressure of fast-charging capacity in major logistics nodes. Based on these findings, the paper proposes targeted recommendations for hub-oriented fast-charging deployment, fleet–charging coordination, and data-driven governance. The study provides empirical evidence for improving the spatial planning and refined governance of urban freight energy infrastructure.