Speaker
Description
Global warming, air pollution, and natural resource depletion have all emerged as urgent global environmental challenges in recent years. Transport electrification is a critical step toward energy conservation and emission reduction. However, the central challenge for electrifying transportation remains insufficient and unsuitable configurations of public charging infrastructure. Electric taxis (e-taxis) are considered a pioneering and critical component of transport electrification. The widespread usage of e-taxis has produced massive charging demand, emphasizing the importance of charging infrastructure development. In light of this, this study evaluates the spatial layout of public charging stations for e-taxis and provide insights for public charging infrastructure planning using GPS trajectory data.
This study has four main sections. First, e-taxi drivers' charging events are extracted from the large-scale GPS trajectory data of a fully electrified taxi fleet. Second, e-taxi drivers' travel time to charging station is introduced as the key evaluation indicator of the spatial layout of public charging stations for e-taxis. Third, the spatial autocorrelation analysis is conducted to examine the spatial patterns of travel time to charging station from global and local perspectives. Fourth, the geographically weighted regression model is developed to investigate the influencing factors of e-taxi drivers' travel time to charging station, particularly considering spatial heterogeneity.
The main findings of this study can be summarized as follows: First, e-taxi drivers' travel time to charging stations is spatially clustered rather than evenly distributed over space. E-taxi drivers in central districts have a superior access to public charging stations. The public charging infrastructure in the northwest of Shenzhen should be enhanced to achieve a more balanced distribution of charging resources. Second, e-taxi drivers' travel time to charging station is influenced by factors including charging station attributes, socio-demographic factors, built environment factors, and taxi trip density. Third, the relationships between e-taxi drivers' travel time to charging stations and its influencing factors exhibit spatial heterogeneity. Targeted strategies and policies should be implemented in different areas to optimize the spatial layout of public charging facilities effectively. Research findings can offer references for future research as well as the spatial planning of public charging infrastructure.
| Keywords | Electric mobility; e-taxi; charging behavior; charging infrastructure planning; GPS data |
|---|---|
| Best Congress Paper Award | Yes |
