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Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

Author

Listed:
  • Sen Ma

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

  • Jiangbi Hu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

  • Ershun Ma

    (Shenzhen–Zhongshan Bridge Management Center, Zhongshan 528400, China)

  • Weicong Li

    (Shenzhen–Zhongshan Bridge Management Center, Zhongshan 528400, China)

  • Ronghua Wang

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

The tunnel is an important component of freeway operation safety, and its classification method is the foundation of a refined management of operation safety. At present, the impact of different categories of tunnels on driver safety, comfort, and driving behavior under naturalistic driving conditions is not clear, and there is a lack of classification methods for tunnels of different lengths in their operation stages. This paper was based on the driving workload, which effectively expresses the safety and comfort of drivers. In this context, naturalistic driving experiments in 13 freeways and 98 tunnels with 36 participants were carried out. The DDTW+K-Means++ algorithm, which is suitable for drivers’ driving workload time series data, was used for a clustering analysis of the tunnels. According to the length of the tunnel, the operation-stage tunnels were divided into three categories: short tunnels (<450 m), general tunnels (450~4000 m), and long tunnels (>4000 m). The length of the tunnel had a positive correlation with the drivers’ driving workload, while there was a negative correlation with the vehicle running speed, and the range of changes in the drivers’ driving workload and operation safety risks in general tunnels and long tunnels was higher than that in short tunnels. Road and environmental conditions are important factors affecting the driving workload. The entrance area, the exit area of tunnels, and the middle area of long tunnels are high-risk sections in the affected area of the tunnel. These research results are of great significance for the operation safety management of freeway tunnels.

Suggested Citation

  • Sen Ma & Jiangbi Hu & Ershun Ma & Weicong Li & Ronghua Wang, 2023. "Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort," Sustainability, MDPI, vol. 15(15), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11914-:d:1209369
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    References listed on IDEAS

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    1. Yongzheng Yang & Zhigang Du & Fangtong Jiao & Fuquan Pan, 2021. "Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel," IJERPH, MDPI, vol. 18(18), pages 1-18, September.
    2. Xiaoling Xu & Xuejian Kang & Xiaoping Wang & Shuai Zhao & Chundi Si, 2022. "Research on Spiral Tunnel Exit Speed Prediction Model Based on Driver Characteristics," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    3. Fangtong Jiao & Zhigang Du & Haoran Zheng & Shoushuo Wang & Lei Han & Can Chen, 2021. "Visual Characteristics of Drivers at Different Sections of an Urban Underpass Tunnel Entrance: An Experimental Study," Sustainability, MDPI, vol. 13(9), pages 1-12, May.
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    Cited by:

    1. Xiaoping Zhao & Kai Shen & Zhenlong Mo & Yunqiang Xue & Chenhui Xue & Shuwei Zhang & Qian Yu & Pengfei Zhang, 2023. "Impact of Road Central Greening Configuration on Driver Eye Movements: A Study Based on Real Vehicle Experiments," Sustainability, MDPI, vol. 15(24), pages 1-17, December.

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