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A Dynamic Space-Time Network Flow Model for City Traffic Congestion

Author

Listed:
  • Daniel J. Zawack

    (American Airlines, Dallas, Texas)

  • Gerald L. Thompson

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

Abstract

A space-time network is developed that represents traffic flows over time for a capacitated road transportation system having one-way and two-way streets. Traffic signal lights are explicitly incorporated into the network structure so that total travel time is a piecewise linear convex function of the number of units traveling on the streets. Hence congestion effects are explicitly considered while maintaining the linear nature of the model. The first example presented has one source and one sink. There is a unimodal buildup of traffic at the source (say a factory) which enters the street network as quickly as its capacity permits and proceeds through the network, stopping at red lights when necessary, toward the sink (a residential area). Two efficient solution methods are used: a network flow solution suitable for a multiple-source single-destination network, and a shortest path solution suitable only for a single-source single-destination network. Computations show that the arrival rate has multiple peaks which are induced by the stop lights. The second example has multiple sources and one sink and gives similar results, except that the arrival rate has a single board peak which is due to the extreme symmetry of the constraints of the problem.

Suggested Citation

  • Daniel J. Zawack & Gerald L. Thompson, 1987. "A Dynamic Space-Time Network Flow Model for City Traffic Congestion," Transportation Science, INFORMS, vol. 21(3), pages 153-162, August.
  • Handle: RePEc:inm:ortrsc:v:21:y:1987:i:3:p:153-162
    DOI: 10.1287/trsc.21.3.153
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    Citations

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    Cited by:

    1. Tong, C. O. & Wong, S. C., 2000. "A predictive dynamic traffic assignment model in congested capacity-constrained road networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(8), pages 625-644, November.
    2. Martin Durbin & Karla Hoffman, 2008. "OR PRACTICE---The Dance of the Thirty-Ton Trucks: Dispatching and Scheduling in a Dynamic Environment," Operations Research, INFORMS, vol. 56(1), pages 3-19, February.
    3. Jeffery L. Kennington & Charles D. Nicholson, 2010. "The Uncapacitated Time-Space Fixed-Charge Network Flow Problem: An Empirical Investigation of Procedures for Arc Capacity Assignment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 326-337, May.
    4. Nathan Preuss & Lin Guo & Janet K. Allen & Farrokh Mistree, 2022. "Improving Patient Flow in a Primary Care Clinic," SN Operations Research Forum, Springer, vol. 3(3), pages 1-22, September.
    5. Mahmoudi, Monirehalsadat & Chen, Junhua & Shi, Tie & Zhang, Yongxiang & Zhou, Xuesong, 2019. "A cumulative service state representation for the pickup and delivery problem with transfers," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 351-380.
    6. Zhikang Bao & Yifu Ou & Shuangzhou Chen & Ting Wang, 2022. "Land Use Impacts on Traffic Congestion Patterns: A Tale of a Northwestern Chinese City," Land, MDPI, vol. 11(12), pages 1-17, December.
    7. Yang, Hai & Meng, Qiang, 1998. "Departure time, route choice and congestion toll in a queuing network with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 32(4), pages 247-260, May.
    8. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
    9. Huang, Hai-Jun & Xu, Gang, 1998. "Aggregate scheduling and network solving of multi-stage and multi-item manufacturing systems," European Journal of Operational Research, Elsevier, vol. 105(1), pages 52-65, February.

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