Traffic safety analysis at interchange exits using the surrogate measure of aggressive driving behavior and speed variation

Abstract

Because of heavy traffic on urban expressways, the exits of expressway interchanges have become accident-prone sites. This study explores the impacts of various traffic control devices and road conditions on road safety at interchange exits based on driving behavior data from navigation software. The traffic order index (TOI) based on driving behavior and speed variation is used to evaluate road safety. The general safety characteristics and partitioned safety characteristics of interchange exit sections for different traffic control devices and under different road conditions were described, and a structural equation model (SEM) was constructed to observe the influences of the traffic control devices, road conditions, congestion degree, and time on road safety. The results show that traffic control devices (the number of warning signs, number of advance exit signs and complexity of diagrammatic guide signs) and road conditions (the number of lanes and merging conflicts within 500 m) have significant influences on the road safety of interchange exits. Road conditions have the greatest impact on the safety of interchange exits, followed by the congestion index, traffic control devices, and time. The results could help traffic management departments reconstruct or rehabilitate traffic control devices and enable reasonable road planning at interchange exits. The safety evaluation method for traffic control devices and road conditions based on driving behavior data collected from navigation software could be further used on other roads.

Keywords:

• Traffic safety
• road conditions
• traffic control devices
• interchange
• driving behavior
• surrogate indicator
• structural equation modeling

Additional information

Funding

This work was supported by the project funded by the China Postdoctoral Science Foundation (NO. 2021M690272), National Natural Science Foundation of China (NO. 52072012), Transportation Engineering of Beijing University of Technology (NO. 2020BJUT2T02) and the Chaoyang District Postdoctoral Science Foundation, the Joint Laboratory for Future Transport and Urban Computing of AutoNavi, and the Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology.