Risk Tendency Classification of Drivers of Road Transport Vehicles of Hazardous Materials

被引：0
|
作者：
Li S.-X. [1 ]
Xing Z.-Y. [2 ]
Qian D.-L. [1 ]
Li P.-C. [1 ]
Yuan M. [1 ]
机构：
[1] Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing
[2] Road Transport Bureau of Hainan Provincial, Haikou
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2023年 / 23卷 / 03期
基金：
中国国家自然科学基金;
关键词：
driver classification; driving style; inhibition control; risk tendency; road transport vehicles of hazardous materials; traffic engineering;
D O I：
10.16097/j.cnki.1009-6744.2023.03.018
中图分类号：
学科分类号：
摘要：
In order to strengthen the safety risk source control of road transport vehicles of hazardous materials (RTVHM), this study fully excavates the trajectory big data, monitoring data, and other multi-source heterogeneous traffic data to study the risk tendency classification of RTVHM drivers. Based on the driving behavior patterns and environmental characteristics contained in the widely available GPS trajectory data, this study introduces the concept of time-varying random volatility and extracts five measures of speed volatility to construct an attribute feature set that characterizes driving style. Coupled with the characteristics of behavior inhibition control, cognitive inhibition control, and physiological load, risk tendency classification indexes are established for RTVHM drivers. The weight of each index is calculated based on the CRITIC method, and the four kinds of attributes describing the RTVHM drivers are scored by the VIKOR algorithm. The risk tendency classification model based on the K-medoids clustering algorithm is established. The results showed that using the classification model, RTVHM drivers were divided into four categories. Among them, drivers with aggressive driving styles and weak behavior inhibition control showed greater speed fluctuations and more vehicle control alarms in face of congested roads and bad weather. Drivers with weak cognitive inhibition control had more distracted duration, allocated more attention to distractions, and diverted attention more frequently between distraction objects and road conditions ahead. Fatigue drivers showed more fatigue alarms and overtime driving alarms, and bear greater physiological loads. The research results can provide a theoretical basis for the identification and risk assessment of the main risk tendency types of RTVHM drivers. © 2023 Science Press. All rights reserved.
引用
收藏
页码：161 / 173
页数：12
相关论文
共 24 条
  • [1] NIU S F, LI G Q, ZHANG S W., Driving Risk Assessment Model of Commercial Drivers Based on Satellite-positioning Data, China Journal of Highway and Transport, 33, 6, pp. 202-211, (2020)
  • [2] QIN W W, LI H, LI W, GU J J, Et al., A Review of Truck Driving Behavior and Safety, Journal of Transportation Systems Engineering and Information Technology, 22, 5, pp. 55-74, (2022)
  • [3] XU T, ZHANG X, ZHANG Y K, Et al., A predictive and forecast model of the risk perception type of the vehicle drivers, Journal of Safely and Environment, 19, pp. 1273-1281, (2019)
  • [4] SHEN X Y, HAN X Q, TANG Y, Et al., Driver risk assessment of dangerous goods transportation based on dynamic monitoring data, Journal of Safely and Environment, 22, pp. 2017-2023, (2022)
  • [5] CHEN C, LIU Q, WANG X, Et al., semi-Traj2Graph Identifying Fine-Grained Driving Style With GPS Trajectory Data via Multi-Task Learning, IEEE Transactions on Big Data, 8, 6, pp. 1550-1565, (2022)
  • [6] MOHAMMADNAZAR A, ARVIN R, KHATTAK A J., Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning, Transportation Research Part C Emerging Technologies, 122, (2021)
  • [7] HOU H J, JIN L S, GUAN Z W, Et al., Effects of Driving Style on Driver Behavior, China Journal of Highway and Transport, 31, 4, pp. 18-27, (2018)
  • [8] WANG W, Xi J, ZHAO D., Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches, IEEE Transactions on Intelligent Transportation Systems, 20, 8, pp. 2986-2998, (2019)
  • [9] MIOTTI M, NEEDELL Z A, RAMAKRISHNAN S, Et al., Quantifying the impact of driving style changes on light-duty vehicle fuel consumption, Transportation Research Part D: Transport and Environment, 98, (2021)
  • [10] QIN W W, YAN Q Y, GU J J, Et al., Driving Style Recognition and Quantification for Heavy-duty Truck Drivers, Journal of Transportation Systems Engineering and Information Technology, 22, 4, pp. 137-148, (2022)
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