Citation

Guo M, Zhao X, Yao Y, Yang H, Qi J, Su Y. J. Transp. Saf. Secur. 2024; 16(2): 130-156.

Copyright

(Copyright © 2024, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2023.2189340

PMID

unavailable

Abstract

This research investigates the impacts of the driver's risky driving behavior on traffic crash risk detection by constructing four models, including models with and without the use of risky driving behavior data before and during crash. The major findings are as follows. First, the accuracy of the model was improved by adding risky driving behavior variables to the traffic crash risk detection model. In the optimal model, the accuracy, recall, precision, false alarm rate, and the missing report rate are 93.0%, 91.2%, 77.5%, 6.6%, and 8.8% respectively. Second, the impact of traffic flow variables on crash risk in the model was modified by introducing risky driving behavior variables. Among them, the impact of traffic volume on crash risk increases, while the relationship between the average speed, congestion index, and crash risk decreases. Third, the effects of changes in risky driving behavior and traffic flow on the risk of traffic crashes are different at the time slices and space segments, and this effect relationship is characterized by nonlinearity. The results demonstrate the influence of temporal and spatial characteristics of risky driving behaviors on crash risk, which supports detection of traffic crash risks in time and develops crash prevention measures.

Language: en

Keywords

CatBoost; Crash risk detection; risky driving behavior; SHAP; traffic flow