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Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level

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  • Saifuzzaman, Mohammad
  • Zheng, Zuduo
  • Haque, Md. Mazharul
  • Washington, Simon

Abstract

This paper provides a detailed understanding of the mechanism of traffic hysteresis and traffic oscillations from the driver behavior perspective. Microscopic evaluation of trajectories inside seven selected oscillations is performed to obtain a comprehensive picture of these puzzling phenomena. A new method based on driver's task difficulty (TD) profile is proposed to capture changes in driver behavior in response to the disturbance caused by traffic oscillations. A close connection between the TD profile and evolution (such as formation and growth) of the stop-and-go traffic oscillations is found. Furthermore, driver behaviors inside the oscillations are identified based on driver's TD profile, and their connection with hysteresis magnitudes is established. Finally, a generalized linear model suggests that variables related to traffic flow and driver characteristics are significant predictors of hysteresis magnitude. One noteworthy finding is that, the bigger the difference between the average TD levels between deceleration and acceleration phases of a vehicle trajectory, the larger the hysteresis magnitude becomes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:105:y:2017:i:c:p:523-538
    DOI: 10.1016/j.trb.2017.09.023
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    Cited by:

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    3. Calvert, Simeon C. & Schakel, Wouter J. & van Lint, J.W.C., 2020. "A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 42-63.
    4. Mohammadian, Saeed & Zheng, Zuduo & Haque, Md. Mazharul & Bhaskar, Ashish, 2021. "Performance of continuum models for realworld traffic flows: Comprehensive benchmarking," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 132-167.
    5. van Lint, J.W.C. & Calvert, S.C., 2018. "A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 63-86.
    6. Yao, Handong & Li, Qianwen & Li, Xiaopeng, 2020. "A study of relationships in traffic oscillation features based on field experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 339-355.
    7. Zheng, Shi-Teng & Jiang, Rui & Tian, Jun-Fang & Zhang, H.M. & Li, Zhen-Hua & Gao, Lan-Da & Jia, Bin, 2021. "Experimental study on properties of lightly congested flow," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 1-19.
    8. Mattas, K. & Albano, G. & Donà, R. & He, Y. & Ciuffo, B., 2023. "On the Relationship between Traffic Hysteresis and String Stability of Vehicle Platoons," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    9. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2020. "The relationship between car following string instability and traffic oscillations in finite-sized platoons and its use in easing congestion via connected and automated vehicles with IDM based control," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 58-83.

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