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Freeway traffic oscillations: Microscopic analysis of formations and propagations using Wavelet Transform

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  • Zheng, Zuduo
  • Ahn, Soyoung
  • Chen, Danjue
  • Laval, Jorge

Abstract

In this paper we identify the origins of stop-and-go (or slow-and-go) driving and measure microscopic features of their propagations by analyzing vehicle trajectories via Wavelet Transform. Based on 53 oscillation cases analyzed, we find that oscillations can be originated by either lane-changing maneuvers (LCMs) or car-following (CF) behavior. LCMs were predominantly responsible for oscillation formations in the absence of considerable horizontal or vertical curves, whereas oscillations formed spontaneously near roadside work on an uphill segment. Regardless of the trigger, the features of oscillation propagations were similar in terms of propagation speed, oscillation duration, and amplitude. All observed cases initially exhibited a precursor phase, in which slow-and-go motions were localized. Some of them eventually transitioned into a well-developed phase, in which oscillations propagated upstream in queue. LCMs were primarily responsible for the transition, although some transitions occurred without LCMs. Our findings also suggest that an oscillation has a regressive effect on car-following behavior: a deceleration wave of an oscillation affects a timid driver (characterized by larger response time and/or minimum spacing) to become less timid and an aggressive driver less aggressive, although this change may be short-lived. An extended framework of Newell’s CF model is able to describe the regressive effect with two additional parameters with reasonable accuracy, as verified using vehicle trajectory data.

Suggested Citation

  • Zheng, Zuduo & Ahn, Soyoung & Chen, Danjue & Laval, Jorge, 2011. "Freeway traffic oscillations: Microscopic analysis of formations and propagations using Wavelet Transform," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1378-1388.
  • Handle: RePEc:eee:transb:v:45:y:2011:i:9:p:1378-1388
    DOI: 10.1016/j.trb.2011.05.012
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    References listed on IDEAS

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    1. Li, Xiaopeng & Peng, Fan & Ouyang, Yanfeng, 2010. "Measurement and estimation of traffic oscillation properties," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 1-14, January.
    2. Zheng, Zuduo & Ahn, Soyoung & Chen, Danjue & Laval, Jorge, 2011. "Applications of wavelet transform for analysis of freeway traffic: Bottlenecks, transient traffic, and traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 372-384, February.
    3. Kim, T. & Zhang, H.M., 2008. "A stochastic wave propagation model," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 619-634, August.
    4. Castillo, Jose M. del, 2001. "Propagation of perturbations in dense traffic flow: a model and its implications," Transportation Research Part B: Methodological, Elsevier, vol. 35(4), pages 367-389, May.
    5. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    6. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
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    Citations

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    Cited by:

    1. Oh, Simon & Yeo, Hwasoo, 2015. "Impact of stop-and-go waves and lane changes on discharge rate in recovery flow," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 88-102.
    2. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    3. Chen, Danjue & Laval, Jorge & Zheng, Zuduo & Ahn, Soyoung, 2012. "A behavioral car-following model that captures traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 744-761.
    4. Taylor, Jeffrey & Zhou, Xuesong & Rouphail, Nagui M. & Porter, Richard J., 2015. "Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 59-80.
    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. 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.
    7. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    8. Chen, Danjue & Ahn, Soyoung & Laval, Jorge & Zheng, Zuduo, 2014. "On the periodicity of traffic oscillations and capacity drop: The role of driver characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 117-136.
    9. repec:eee:transb:v:105:y:2017:i:c:p:523-538 is not listed on IDEAS
    10. Chen, Danjue & Ahn, Soyoung & Hegyi, Andreas, 2014. "Variable speed limit control for steady and oscillatory queues at fixed freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 340-358.
    11. Zheng, Zuduo & Su, Dongcai, 2016. "Traffic state estimation through compressed sensing and Markov random field," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 525-554.

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