Dynamic driving risk in highway tunnel groups based on pupillary oscillations

被引:9
|
作者
Zheng, Haoran [1 ,2 ]
Du, Zhigang [1 ]
Wang, Shoushuo [3 ]
机构
[1] Wuhan Univ Technol, Sch Transportat & Logist Engn, 1178 Heping Rd, Wuhan 430063, Hubei, Peoples R China
[2] Eindhoven Univ Technol, Dept Built Environm, Groene Loper 3, NL-5612 AE Eindhoven, Noord Brabant, Netherlands
[3] Guangzhou Maritime Univ, Sch Port & Shipping Management, 101 Hongshan 3rd Rd, Guangzhou 510725, Guangdong, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Highway Tunnel Group; PPAV; Pupillary Oscillations; Driving Risks; Whipping Effect; ROAD TUNNEL; PERCEPTION; FREEWAY; DESIGN; SYSTEM; IMPACT; SIZE;
D O I
10.1016/j.aap.2023.107414
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
This study aims to understand the dynamic changes in driving risks in highway tunnel groups. Real-world driving experiments were conducted, collecting pupil area data to measure pupil size oscillations using the Percentage of Pupil Area Variable (PPAV) metric. The analysis focused on investigating relative pupil size fluctuations to explore trends in driving risk fluctuations within tunnel groups. The objective was to identify accident-prone areas and key factors influencing driving risks, providing insights for safety improvements. The findings revealed an overall "whipping effect" phenomenon in driving risk changes within tunnel groups. Differences were observed between interior tunnel areas and open sections, including adjacent, approach, and departure zones. Higher driving risks were associated with locations closer to the tail end of the tunnel group and shorter exit departure sections. Targeted safety improvement designs should consider fluctuation patterns in different directions, with attention to tunnels at the tail end. In open sections, increased travel distance and lengths of upstream and downstream tunnels raised driving risks, while longer open zones improved driving risks. Driving direction and sequence had minimal impact on risks. By integrating driver vision, tunnel characteristics, and the environment, this study identified high-risk areas and critical factors, providing guidance for monitoring and improving driving risks in tunnel groups. The findings have practical implications for the operation and safety management of tunnel groups.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Driving Risk Identification of Truck Drivers Based on China's Highway Toll Data
    Yang, Zhenzhen
    SUSTAINABILITY, 2024, 16 (05)
  • [22] STACKELBERG GAME BASED MODEL OF HIGHWAY DRIVING
    Yoo, Je Hong
    Langari, Reza
    PROCEEDINGS OF THE ASME 5TH ANNUAL DYNAMIC SYSTEMS AND CONTROL DIVISION CONFERENCE AND JSME 11TH MOTION AND VIBRATION CONFERENCE, DSCC 2012, VOL 1, 2013, : 499 - 508
  • [23] Model based data verification in a highway tunnel
    Sládek, O
    Kurka, L
    Ferkl, L
    Sebek, M
    Proceedings of the 7th WSEAS International Conference on Automatic Control, Modeling and Simulation, 2005, : 410 - 415
  • [24] Study on the critical driving speed of the tunnel boring machine cutterhead based on the dynamic stability
    Qu, Chuanyong, 1600, Chinese Mechanical Engineering Society (50):
  • [25] A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving
    Long, Haitao
    Lu, Xiangqian
    Ma, Chunchi
    Li, Tianbin
    Yan, Wenjin
    Zhang, Hang
    Dai, Kunkun
    FRONTIERS IN EARTH SCIENCE, 2023, 11
  • [26] Risk Evaluation Model of Highway Tunnel Portal Construction Based on BP Fuzzy Neural Network
    Deng, Xianghui
    Xu, Tian
    Wang, Rui
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2018, 2018
  • [27] Driving Risk Quantization Model of Two-Lane Highway Based on Visual Alignment Information
    Fang, Yong
    Shi, Qi
    INTERNATIONAL CONFERENCE ON TRANSPORTATION AND DEVELOPMENT 2020: TRANSPORTATION SAFETY, 2020, : 243 - 253
  • [28] Driving Style-based Sensitivity Analysis of Driving Risk Field in Mountain Highway Sections Passing Through Villages and Towns
    Ji, Xiaofeng
    Wang, Jian
    Xu, Yinghao
    Lu, Mengyuan
    Qin, Wenwen
    Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2024, 24 (06): : 316 - 325
  • [29] Dynamic Analysis of Deep Water Highway Tunnel under Ocean Current
    Fang, Li
    Li, Hong
    Li, Bin
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [30] Research on dynamic construction mechanics of four-lane highway tunnel
    Wu, Mengjun
    Huang, Lunhai
    Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 2006, 25 (SUPPL. 1): : 3057 - 3062