Understanding Human Mobility Patterns from Urban Energy Consumption Records: Through Electric Vehicle Charging Records

Abstract

This study pioneers a novel method to interpret urban energy consumption through human mobility patterns, utilizing electric vehicle (EV) charging data as a proxy. By applying algorithms like DBSCAN and a pre-trained human mobility model, the research effectively transforms EV charging logs into a detailed map of urban movement. Analyzing data from Shanghai, the study successfully correlates these synthesized mobility trajectories with actual human movement patterns, revealing a strong interplay between EV charging behavior and urban dynamics. This innovative approach not only offers fresh insights into urban energy dynamics but also respects individual privacy, marking a significant advancement in the field of urban planning and sustainable development.