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Driver Behavior Mechanisms and Conflict Risk Patterns in Tunnel-Interchange Connecting Sections: A Comprehensive Investigation Based on the Behavioral Adaptation Theory

Author

Listed:
  • Chenwei Gu

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an 710064, China)

  • Xingliang Liu

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an 710064, China
    College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Nan Mao

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an 710064, China)

Abstract

Tunnel-interchange sections are characterized by complex driving tasks and frequent traffic conflicts, posing substantial challenges to overall safety and efficiency. Enhancing safety in these areas is crucial for the sustainability of traffic systems. This study applies behavior adaptation theory as an integrated framework to examine the impact of environmental stimuli on driving behavior and conflict risk in small-spaced sections. Through driving simulation, 19 observation indicators are collected, covering eye-tracking, heart rate, subjective workload, driving performance, and conflict risk. The analysis, using single-factor ranking (Shapley Additive Explanation), interaction effects (dependence plots), and multi-factor analysis (Structural Equation Modeling), demonstrates that driving workload and performance dominate the fully mediating effects between external factors and conflict risk. High-load environmental stimuli, such as narrow spacing (≤500 m) and overloaded signage information (>6 units), significantly elevate drivers’ stress responses and impair visual acuity, thereby increasing task difficulty and conflict risk. Critical factors like saccade size, heart rate variability, lane deviation, and headway distance emerge as vital indicators for monitoring and supporting driving decisions. These findings provide valuable insights for the operational management of small-spacing sections and enhance the understanding of driving safety in these areas from a human factor perspective.

Suggested Citation

  • Chenwei Gu & Xingliang Liu & Nan Mao, 2024. "Driver Behavior Mechanisms and Conflict Risk Patterns in Tunnel-Interchange Connecting Sections: A Comprehensive Investigation Based on the Behavioral Adaptation Theory," Sustainability, MDPI, vol. 16(19), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8701-:d:1494698
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    References listed on IDEAS

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    1. Varotto, Silvia F. & Farah, Haneen & Toledo, Tomer & van Arem, Bart & Hoogendoorn, Serge P., 2018. "Modelling decisions of control transitions and target speed regulations in full-range Adaptive Cruise Control based on Risk Allostasis Theory," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 318-341.
    2. Tianzheng Wei & Tong Zhu & Chenxin Li & Haoxue Liu, 2022. "Analysis of hazard perception characteristics based on driving behavior considering overt and covert hazard scenarios," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-19, April.
    3. Saifuzzaman, Mohammad & Zheng, Zuduo & Mazharul Haque, Md. & Washington, Simon, 2015. "Revisiting the Task–Capability Interface model for incorporating human factors into car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 1-19.
    4. Premkumar, G. & Bhattacherjee, Anol, 2008. "Explaining information technology usage: A test of competing models," Omega, Elsevier, vol. 36(1), pages 64-75, February.
    5. Huan Liu & Jinliang Xu & Xiaodong Zhang & Chao Gao & Rishuang Sun, 2022. "Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing," IJERPH, MDPI, vol. 19(12), pages 1-18, June.
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