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The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

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Listed:
  • Zhanji Zheng

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 21189, Jiangsu, China)

  • Qiaojun Xiang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 21189, Jiangsu, China)

  • Xin Gu

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Yongfeng Ma

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 21189, Jiangsu, China)

  • Kangkang Zheng

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 21189, Jiangsu, China)

Abstract

Urban expressway weaving sections suffer from a high crash risk in urban transportation systems. Studying driving behavior is an important approach to solve safety and efficiency issues at expressway weaving sections. This study aimed to investigate the influence of drivers’ individual differences on diverging behavior at expressway weaving sections. First, a k-means cluster analysis of 650 questionnaires was performed, to classify drivers into three categories: aggressive, conservative and normal. Then, the driving behavior of 45 drivers from the three categories was recorded in a driving simulator and analyzed by an analysis of variance. The results show that different types of drivers have different driving behaviors at weaving sections. Aggressive drivers have a higher mean speed and mean longitudinal deceleration, followed by normal and conservative drivers. Significant differences in the range of lane-change positions were found between 100, 150 and 200 m of weaving length for the same type of drivers, and the duration of weaving for aggressive drivers was significantly smaller than for normal and conservative drivers. A significant correlation was found between lane-change position and weaving duration. These results can help traffic engineers to propose effective control strategies for different types of drivers, to improve the safety of weaving sections.

Suggested Citation

  • Zhanji Zheng & Qiaojun Xiang & Xin Gu & Yongfeng Ma & Kangkang Zheng, 2020. "The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:25-:d:466618
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    References listed on IDEAS

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    1. Zheng, Jihu & Zhou, Yan & Yu, Rujie & Zhao, Dongchang & Lu, Zifeng & Zhang, Peng, 2019. "Survival rate of China passenger vehicles: A data-driven approach," Energy Policy, Elsevier, vol. 129(C), pages 587-597.
    2. Daniel (Jian) Sun & Lily Elefteriadou, 2014. "A Driver Behavior-Based Lane-Changing Model for Urban Arterial Streets," Transportation Science, INFORMS, vol. 48(2), pages 184-205, May.
    3. Golob, Thomas F. & Recker, Wilfred W. & Alvarez, Veronica M., 2004. "Safety aspects of freeway weaving sections," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 35-51, January.
    4. Xinhua Mao & Changwei Yuan & Jiahua Gan & Shiqing Zhang, 2019. "Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China," IJERPH, MDPI, vol. 16(9), pages 1-17, May.
    5. Sun, Shichao & Duan, Zhengyu, 2019. "Modeling passengers’ loyalty to public transit in a two-dimensional framework: A case study in Xiamen, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 295-309.
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