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The Moving and “Phantom” Bottlenecks

Author

Listed:
  • Denos C. Gazis

    (IBM T. J. Watson Research Center, Yorktown Heights, NY 10598)

  • Robert Herman

    (The University of Texas at Austin, Austin, TX 78712)

Abstract

When a slow-moving vehicle occupies one of the lanes of a multilane highway, it often causes queueing behind it, not unlike one caused by an actual stoppage on that lane. This happens when the traffic flow rate upstream from the slow vehicle exceeds a certain critical value. We define this situation as the case of the Moving Bottleneck. There are other situations when people caught in a traffic jam slowly advance to the point of a suspected breakdown or other obstruction, only to find out that no such obstruction exists but suddenly the flow of traffic is free and rapid. We define this situation as that of the Phantom Bottleneck. We present a model describing these two situations on the basis of reasonable assumptions about the behavior of drivers whose movement is obstructed by the slow vehicle. Using a phenomenological relationship between flow and concentration, we derive formulas describing the behavior of the moving queue caused by the obstruction. Finally, we suggest procedures for validating the model and using it for a rational approach to the design of allowable minimum speeds on highways.

Suggested Citation

  • Denos C. Gazis & Robert Herman, 1992. "The Moving and “Phantom” Bottlenecks," Transportation Science, INFORMS, vol. 26(3), pages 223-229, August.
    • Handle: RePEc:inm:ortrsc:v:26:y:1992:i:3:p:223-229
    DOI: 10.1287/trsc.26.3.223
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    Cited by:

    1. Bouadi, Marouane & Jia, Bin & Jiang, Rui & Li, Xingang & Gao, Zi-You, 2022. "Stability analysis of stochastic second-order macroscopic continuum models and numerical simulations," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 193-209.
    2. Simoni, Michele D. & Claudel, Christian G., 2017. "A fast simulation algorithm for multiple moving bottlenecks and applications in urban freight traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 238-255.
    3. Chubo Xu & Jianxiao Ma & Xiang Tang, 2022. "A Simulation-Based Study of the Influence of Low-Speed Vehicles on Expressway Traffic Safety," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    4. Yang, Liu & Zheng, Jianlong & Cheng, Yang & Ran, Bin, 2019. "An asymmetric cellular automata model for heterogeneous traffic flow on freeways with a climbing lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Blanch Micó, Mª Teresa & Lucas Alba, Antonio & Bellés Rivera, Teresa & Ferruz Gracia, Ana Mª & Melchor Galán, Óscar M. & Delgado Pastor, Luis C. & Ruíz Jiménez, Francisco & Chóliz Montañés, Mariano, 2018. "Car following: Comparing distance-oriented vs. inertia-oriented driving techniques," Transport Policy, Elsevier, vol. 67(C), pages 13-22.
    6. Daganzo, Carlos F., 1993. "The Spatial Evolution of Traffic Under the Two Wave Speed Assumption: A Shortcut Procedure and Some Observations," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0kv1x3rk, Institute of Transportation Studies, UC Berkeley.

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