IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v31y1997i2p83-102.html
   My bibliography  Save this article

A continuum theory of traffic dynamics for freeways with special lanes

Author

Listed:
  • Daganzo, Carlos F.

Abstract

This paper presents a generalized theory of kinematic waves for freeways with two vehicle types and a set of lanes reserved for one of the vehicle classes. The theory is not restricted to freeways on which the special lanes are clearly identified by signs and pavement markings; e.g. for high occupancy vehicles. It may also apply if the restrictions are self-imposed, such as would occur on a freeway segment upstream of a busy off-ramp where the existing traffic naturally avoids the 'far-side' lanes. Of particular interest are oversaturated time periods because the original theory of kinematic waves proposed by Lighthill and Whitham [Proceedings Royal Society, A 229, 281-345 (1955)] and Richards (1956) does not recognize that different traffic conditions (queues and speeds) may arise on the two sets of lanes, and that these may affect the two vehicle classes in different ways. It should be intuitive that whether a single coalesced queue forms on both sets of lanes or separate queues form on each set should depend on the traffic composition by vehicle class. In an attempt to furnish a reasonable depiction of these phenomena, a pair of conservation-type partial differential equations in the densities of the two vehicle types is used to model the freeway. In their simplest form, the equations only require the introduction of one additional parameter (representing the fraction of lanes allocated to each vehicle type) over the basic kinematic wave theory. The model is attractive because the nature of its solution can be described in complete physical detail by means of simple intuitive diagrams that show how the simple kinematic wave model is improved. The paper also introduces an exact solution method for problems with piece-wise constant initial data that can either be applied graphically or numerically. For general (piece-wise smooth) data, the numerical technique can be used to approximate the true solution as closely as desired in a way that does not deteriorate with time. A less precise finite-difference approach that always moves vehicles forward is also presented.

Suggested Citation

  • Daganzo, Carlos F., 1997. "A continuum theory of traffic dynamics for freeways with special lanes," Transportation Research Part B: Methodological, Elsevier, vol. 31(2), pages 83-102, April.
  • Handle: RePEc:eee:transb:v:31:y:1997:i:2:p:83-102
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(96)00017-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part III: Multi-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 305-313, August.
    2. 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.
    3. Denos C. Gazis & Robert Herman & George H. Weiss, 1962. "Density Oscillations Between Lanes of a Multilane Highway," Operations Research, INFORMS, vol. 10(5), pages 658-667, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin, Wen-Long, 2010. "A kinematic wave theory of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1001-1021, September.
    2. Gentile, Guido & Meschini, Lorenzo & Papola, Natale, 2007. "Spillback congestion in dynamic traffic assignment: A macroscopic flow model with time-varying bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(10), pages 1114-1138, December.
    3. Seo, Toru & Kawasaki, Yutaka & Kusakabe, Takahiko & Asakura, Yasuo, 2019. "Fundamental diagram estimation by using trajectories of probe vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 40-56.
    4. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    5. Huanping Li & Jian Wang & Guopeng Bai & Xiaowei Hu, 2021. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads," Energies, MDPI, vol. 14(12), pages 1-21, June.
    6. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
    8. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    9. Jin, Wen-Long, 2010. "Continuous kinematic wave models of merging traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1084-1103, September.
    10. Daganzo, Carlos F., 2006. "In traffic flow, cellular automata = kinematic waves," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 396-403, June.
    11. Pedro Cesar Lopes Gerum & Andrew Reed Benton & Melike Baykal-Gürsoy, 2019. "Traffic density on corridors subject to incidents: models for long-term congestion management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 795-831, December.
    12. Jin, Wen-Long, 2018. "Unifiable multi-commodity kinematic wave model," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 639-659.
    13. Armbruster, D. & de Beer, C. & Freitag, M. & Jagalski, T. & Ringhofer, C., 2006. "Autonomous control of production networks using a pheromone approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 104-114.
    14. Yan, Qinglong & Sun, Zhe & Gan, Qijian & Jin, Wen-Long, 2018. "Automatic identification of near-stationary traffic states based on the PELT changepoint detection," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 39-54.
    15. Martínez, Irene & Jin, Wen-Long, 2020. "Optimal location problem for variable speed limit application areas," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 221-246.
    16. Mads Paulsen & Thomas Kjær Rasmussen & Otto Anker Nielsen, 2022. "Including Right-of-Way in a Joint Large-Scale Agent-Based Dynamic Traffic Assignment Model for Cars and Bicycles," Networks and Spatial Economics, Springer, vol. 22(4), pages 915-957, December.
    17. Ruru Xing & Yihan Zhang & Xiaoyu Cai & Jupeng Lu & Bo Peng & Tao Yang, 2023. "Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data," Sustainability, MDPI, vol. 15(8), pages 1-30, April.
    18. Jin, Wen-Long & Zhang, H. Michael, 2013. "An instantaneous kinematic wave theory of diverging traffic," Transportation Research Part B: Methodological, Elsevier, vol. 48(C), pages 1-16.
    19. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:31:y:1997:i:2:p:83-102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.