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Structure of the Transition Zone Behind Freeway Queues

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  • Juan Carlos Muñoz

    (Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, Berkeley, California 94720)

  • Carlos F. Daganzo

    (Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, Berkeley, California 94720)

Abstract

Observations of freeway traffic flow are usually quite scattered about an underlying curve when plotted versus density or occupancy. Although increasing the sampling intervals can reduce the scatter, whenever an experiment encompasses a rush hour with transitions in and out of congestion, some outlying data stubbornly remain beneath the “equilibrium” curve. The existence of these nonequilibrium points is a poorly understood phenomenon that appears to contradict the simple kinematic wave (KW) model of traffic flow. This paper provides a tentative explanation of the phenomenon, based on experimental evidence. The evidence was a FIFO queue that grew and receded over two detector stations, generating typical flow-density scatter plots at both locations. The locations were far from other interacting traffic streams. The data revealed that a transition zone where vehicles decelerated gradually existed immediately behind the queue. The transition zone was quite wide (about 1 km at both locations), moved slowly (approximately with the “shock” velocity of KW theory), and as a result spent many minutes over each detector station. Disequilibrium flow-density points arose only when the transition zone was over the detectors, suggesting that the transition zone explains their occurrence. The disequilibrium points drifted gradually from one branch of the curve to the other, as KW theory would have predicted if “shocks” had a characteristic width equal to the dimension of the transition zone. Nothing was found in the data to contradict this view. This paper also shows that in our case, if one neglects the shocks' physical dimension, the position of every vehicle can be predicted with KW theory to within approximately five vehicle spacings. Thus, it appears that KW theory can predict rather accurately traffic behavior at the back of FIFO queues, i.e., when the lanes are equally attractive to all drivers. We end with a discussion offering some perspective on how the findings of this paper related to the traffic thinking found in the current literature.

Suggested Citation

  • Juan Carlos Muñoz & Carlos F. Daganzo, 2003. "Structure of the Transition Zone Behind Freeway Queues," Transportation Science, INFORMS, vol. 37(3), pages 312-329, August.
    • Handle: RePEc:inm:ortrsc:v:37:y:2003:i:3:p:312-329
    DOI: 10.1287/trsc.37.3.312.16043
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    References listed on IDEAS

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    2. Xianglun Mo & Xiaohong Jin & Jinpeng Tian & Zhushuai Shao & Gangqing Han, 2022. "Research on the Division Method of Signal Control Sub-Region Based on Macroscopic Fundamental Diagram," Sustainability, MDPI, vol. 14(13), pages 1-19, July.
    3. Qian, Wei-Liang & F. Siqueira, Adriano & F. Machado, Romuel & Lin, Kai & Grant, Ted W., 2017. "Dynamical capacity drop in a nonlinear stochastic traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 328-339.
    4. Martin Schönhof & Dirk Helbing, 2007. "Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling," Transportation Science, INFORMS, vol. 41(2), pages 135-166, May.
    5. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.

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