Identifying spatio-temporal pattern of electric vehicles involved traffic accidents

Abstract

The increasing popularity of electric vehicles (EVs) has given rise to concerns regarding emerging safety challenges. To address this, an enhanced spatio-temporal kernel density estimation (STKDE) method is developed, which leverages GIS technologies to analyze the temporal evolution of EV accidents across different locations. Our approach introduces several innovations: (1) The incorporation of network distance to address overestimation associated with the conventional Euclidean distance; (2) Integration of a Severity Index (SI) to rationalize accident weighting; (3) Utilization of a likelihood cross-validation method to determine optimal bandwidths for simplified computation. Additionally, statistical significance tests and a ranking system based on cluster strength are implemented to enhance the accuracy and stability of hotspot identification compared to the traditional kernel density estimation (KDE) method. Empirical analyses reveal spatial trends, indicating a higher likelihood of EV accidents occurring near city administrative centers and along arterial roads. Temporally, our findings show increased daytime hotspot density with distinct morning and evening peaks compared to nighttime. Furthermore, a total of 209 hazardous locations, containing 907 accidents, representing 23.4% of all accidents are filtered. Our improved STKDE approach demonstrates a mean hit rate of 0.553 and a PAI of 5.573, which are 3.5% and 27.5% higher, respectively, than those achieved with SKDEKDE. These insights can assist transportation agencies in implementing targeted interventions and resource allocation strategies.

Keywords:

• traffic safety
• accident hotspots
• spatio-temporal kernel density estimation (STKDE) method
• optimal bandwidths
• statistical significance tests

Acknowledgments

The authors also would like to thank the graduate research assistants at the School of Traffic and Transportation at Beijing Jiaotong University for the assistance in data collection.

Disclosure statement

The authors declare that we have no conflicts of interest. The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The data of this study can be requested from the corresponding author.

Additional information

Funding

This research was supported by funding provided by the National Natural Science Foundation of China (72371017).