TY  - JOUR
PY  - 2023//
TI  - Traffic order analysis of intersection entrance based on aggressive driving behavior data using CatBoost and SHAP
JO  - Journal of transportation engineering, Part A: Systems
A1  - Zhao, Xiaohua
A1  - Qi, Hang
A1  - Yao, Ying
A1  - Guo, Miao
A1  - Su, Yuelong
SP  - e04023037
EP  - e04023037
VL  - 149
IS  - 6
N2  - 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
LA  - en
SN  - 2473-2907
UR  - http://dx.doi.org/10.1061/JTEPBS.0000769
ID  - ref1
ER  -