Citation

Zhao X, Qi H, Yao Y, Guo M, Su Y. J. Transp. Eng. A: Systems 2023; 149(6): e04023037.

Copyright

(Copyright © 2023, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000769

PMID

unavailable

Abstract

Analyzing road risks and developing targeted countermeasures are essential for a safe and orderly traffic flow. However, previous intersection safety analyses were conducted based on crash data. Little research has been conducted on surrogate safety measures based on risky driving behavior. In this study, categorical boosting (CatBoost) and Shapley additive explanation (SHAP) were used to analyze the impact of features on traffic order using a set of multisource data that include roadway geometry, signal control, and land use. The traffic data for intersection entrances in Beijing were collected from navigation systems, field investigations, and application programming interfaces. The model results showed that CatBoost exhibits a prediction accuracy of 83.5%, a recall of 83.5%, and an

Keywords

Categorical boosting (CatBoost); Machine learning; Shapley additive explanation (SHAP); Signalized intersection entrance; Traffic order