Research on Accident Severity Prediction of New Energy Vehicles Based on Cost-Sensitive Fuzzy XGBoost

Shubing Huang (), Xiaoxuan Yin (), Chongming Wang and Kun Wang
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Shubing Huang: Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, China
Xiaoxuan Yin: National Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Chongming Wang: The Center for E-Mobility and Clean Growth, Coventry University, Coventry CV1 5FB, UK
Kun Wang: Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, China

Sustainability, 2025, vol. 17, issue 12, 1-15

Abstract: With the increasing acceptance of green, low-carbon, and sustainable development principles, the rising number of new energy vehicles (NEVs) has raised public concern over traffic safety risks associated with these vehicles. To assist traffic management authorities in efficiently allocating rescue resources, this paper proposes a severity prediction method for the new energy vehicle accidents based on Cost-sensitive Fuzzy XGBoost (CFXGBoost). First, chi-square filtering and wrapper methods are used to extract 20 key features strongly cor-related with accident severity. Then, A fuzzy neural network is employed to combine fuzzy inference results with original features, forming an extended feature set. Moreover, These features are used as inputs to the XGBoost model for severity prediction of the new energy vehicle traffic accidents. Finally, the proposed approach is validated using traffic accident datasets from multiple provinces and cities. Results show that the FXGBoost model achieves a prediction accuracy of 0.92 and outperforms other models in terms of precision, recall, and F1 score, demonstrating its effectiveness in accurately predicting the severity of NEV-related traffic accidents.

Keywords: new energy vehicle accident; accident severity prediction; fuzzy XGBoost; chi-square filtering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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