Research on Road Traffic Safety Risk Assessment Based on the Data of Radar Video Integrated Sensors

Xiaoyu CAI; Zimu LI; Wufeng QIAO; Xiling CHENG; Bo PENG; Dong ZHANG

doi:10.7307/ptt.v37i2.777

Authors

Xiaoyu CAI Chongqing Jiaotong University, College of Smart City
Zimu LI Chongqing Jiaotong University, College of Traffic Transportation
Wufeng QIAO China Construction Seventh Engineering Division Corp.; T. Y. Lin International Engineering Consulting (China) Corp. Ltd; Chongqing Jiaotong University
Xiling CHENG Chongqing Jiaotong University, College of Traffic Transportation
Bo PENG Chongqing Jiaotong University, College of Smart City
Dong ZHANG Chongqing Jiaotong University, College of Traffic Transportation

DOI:

https://doi.org/10.7307/ptt.v37i2.777

Keywords:

traffic engineering, risk estimate, information entropy, unsafe driving behaviour, entropy weight method

Abstract

To accurately prevent and warn of traffic accidents, this article proposes a method for predicting urban road traffic safety risks based on vehicle driving behaviour data and information entropy theory. This method uses data from radar video-integrated sensors to calibrate the thresholds for identifying unsafe driving behaviour, introduces recognition principles and algorithms, and analyses spatiotemporal distribution patterns. By incorporating entropy theory, an evaluation system with traffic safety entropy as the primary indicator and the unsafe driving behaviour rate as the secondary indicator is established. Clustering algorithms determine the classification number and threshold of traffic safety entropy, constructing a tunnel traffic safety risk assessment model, which is validated with road accident data. Using 13 days of data from the left lane of Qingdao Jiaozhou Bay Tunnel, the model divides traffic operation risk into high and low categories based on K-means clustering results of accident and safety entropy data. The study finds that when the safety entropy classification threshold is 0.0507, the classification accuracy is the highest at 92%. These results provide technical support for identifying road traffic safety risk points and preventing accidents.

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