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Analysis of asymmetric driving behavior using a self-learning approach

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  • Wei, Dali
  • Liu, Hongchao

Abstract

This paper presents a self-learning Support Vector Regression (SVR) approach to investigate the asymmetric characteristic in car-following and its impacts on traffic flow evolution. At the microscopic level, we find that the intensity difference between acceleration and deceleration will lead to a ‘neutral line’, which separates the speed-space diagram into acceleration and deceleration dominant areas. This property is then used to discuss the characteristics and magnitudes of microscopic hysteresis in stop-and-go traffic. At the macroscopic level, according to the distribution of neutral lines for heterogeneous drivers, different congestion propagation patterns are reproduced and found to be consistent with Newell’s car following theory. The connection between the asymmetric driving behavior and macroscopic hysteresis in the flow-density diagram is also analyzed and their magnitudes are shown to be positively related.

Suggested Citation

  • Wei, Dali & Liu, Hongchao, 2013. "Analysis of asymmetric driving behavior using a self-learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 1-14.
    • Handle: RePEc:eee:transb:v:47:y:2013:i:c:p:1-14
    DOI: 10.1016/j.trb.2012.09.003
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    References listed on IDEAS

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    1. Gong, Huaxin & Liu, Hongchao & Wang, Bing-Hong, 2008. "An asymmetric full velocity difference car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2595-2602.
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    5. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
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    7. Tordeux, Antoine & Lassarre, Sylvain & Roussignol, Michel, 2010. "An adaptive time gap car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1115-1131, September.
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    Citations

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    Cited by:

    1. Pengcheng Fan & Jingqiu Guo & Haifeng Zhao & Jasper S. Wijnands & Yibing Wang, 2019. "Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    2. Dayi Qu & Shaojie Wang & Haomin Liu & Yiming Meng, 2022. "A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    3. Cheng-Ju Song & Hong-Fei Jia, 2022. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    4. Dali Wei & Changwei Yuan & Hongchao Liu & Dayong Wu & Wesley Kumfer, 2017. "The Impact of Service Refusal to the Supply–Demand Equilibrium in the Taxicab Market," Networks and Spatial Economics, Springer, vol. 17(1), pages 225-253, March.
    5. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    6. Pan, Weixiu & Zhang, Jing & Tian, Junfang & Cui, Fengying & Wang, Tao, 2023. "Analysis of car–following behaviors based on data–driven and theory–driven car–following models: Heterogeneity and asymmetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    7. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    8. Zhao, Fangxia & Shang, HuaYan & Cui, JiHui, 2023. "Role of electric vehicle driving behavior on optimal setting of wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
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    10. Saifuzzaman, Mohammad & Zheng, Zuduo & Haque, Md. Mazharul & Washington, Simon, 2017. "Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 523-538.

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