The Impact of the Parking Spot' Surroundings on Charging Decision: A Data-Driven Approach

The charging behavior of drivers serves as a valuable reference for planning and managing charging facilities. This study examines the influence of surrounding environments on charging decisions using real trajectory data from electric vehicles. It considers the built environment, vehicle conditions, and the nearest charging station attributes. The mixed binary logit model was applied to capture the impact of unobserved heterogeneity. The findings indicate that the number of fast chargers in the charging station, parking prices, dwell time, and shopping services significantly influence charging decisions, while leisure services, scenic spots, and mileage since the last charging exhibit opposite effects. Additionally, factors related to unobserved heterogeneity include the number of fast chargers, parking and charging prices, and residential areas. The interaction effects of random parameters further illustrate the complexity of charging choice behavior. Overall, the results offer valuable insights for the planning and management of charging facilities.