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Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

Author

Listed:
  • Huan Liu

    (School of Highway, Chang’an University, Xi’an 710054, China)

  • Jinliang Xu

    (School of Highway, Chang’an University, Xi’an 710054, China)

  • Xiaodong Zhang

    (Journal Center, Chang’an University, Xi’an 710061, China)

  • Chao Gao

    (School of Highway, Chang’an University, Xi’an 710054, China)

  • Rishuang Sun

    (Shandong Provincia Conmmunications Planing and Design Institute Group Co., Ltd., Jinan 250101, China)

Abstract

The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload ( DW ). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload ( SW ). Objective workload ( OW ) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW , and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DW s in optimizing traffic safety.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7063-:d:834746
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    Citations

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    Cited by:

    1. Lian Xie & Jiaxin Zhang & Rui Cheng, 2023. "Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    2. Rizwan Ullah Faiz & Nordiana Mashros & Sitti Asmah Hassan, 2022. "Speed Behavior of Heterogeneous Traffic on Two-Lane Rural Roads in Malaysia," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    3. 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.

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