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Pilot study of computer-based urban traffic management

Author

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  • Gartner, Nathan H.
  • Gershwin, Stanley B.
  • Little, John D. C.
  • Ross, Paul

Abstract

The objectives of urban traffic management are to make more efficient use of existing transportation resources and provide for the movement of people in an efficient manner through the development of low-cost short-range management strategies. Implementation of such strategies can provide for reduced travel costs, savings of energy, reduced air pollution, and improved safety and convenience for the users of the facilities. It can also help to reduce necessary capital expenditure for new facilities to accommodate urban travel demands in the longer range. This paper presents a modelling framework for multimodal urban traffic management, explicitly modelling the divergent objectives of traffic managers and trip-makers. Facilities analyzed are arterial streets and freeways with their associated access and egress links. Modes considered are private automobiles, carpools, trucks, and buses. Decision parameters may include signal controls, ramp metering rates, priority lane assignments, variable-message signs, etc. Effects of modal performance on mode choice are also included in the analysis. Computational results are reported of a pilot application of the framework using heuristic solution techniques.

Suggested Citation

  • Gartner, Nathan H. & Gershwin, Stanley B. & Little, John D. C. & Ross, Paul, 1980. "Pilot study of computer-based urban traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 14(1-2), pages 203-217.
    • Handle: RePEc:eee:transb:v:14:y:1980:i:1-2:p:203-217
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    Cited by:

    1. José R. Correa & Andreas S. Schulz & Nicolás E. Stier-Moses, 2004. "Selfish Routing in Capacitated Networks," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 961-976, November.
    2. Li, Anna C.Y. & Nozick, Linda & Xu, Ningxiong & Davidson, Rachel, 2012. "Shelter location and transportation planning under hurricane conditions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 715-729.
    3. Evers, Ruth & Proost, Stef, 2015. "The myth of traffic-responsive signal control: Why common sense does not always make sense," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 350-357.
    4. Yu Nie & H. Zhang, 2010. "A Relaxation Approach for Estimating Origin–Destination Trip Tables," Networks and Spatial Economics, Springer, vol. 10(1), pages 147-172, March.
    5. Evers, Ruth & Proost, Stef, 2015. "Optimizing intersections," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 100-119.
    6. Thill, Jean-Claude & Rogova, Galina & Yan, Jun, 2004. "Evaluating Benefits And Costs Of Intelligent Transportation Systems Elements From A Planning Perspective," Research in Transportation Economics, Elsevier, vol. 8(1), pages 571-603, January.
    7. Correa, Jose R. & Schulz, Andreas S. & Stier Moses, Nicolas E., 2003. "Selfish Routing in Capacitated Networks," Working papers 4319-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    8. Bielli, Maurizio & Reverberi, Pierfrancesco, 1996. "New operations research and artificial intelligence approaches to traffic engineering problems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 550-572, August.
    9. Yang, Hai & Yagar, Sam, 1995. "Traffic assignment and signal control in saturated road networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 29(2), pages 125-139, March.
    10. Wong, Jinn-Tsai, 1997. "Basic concepts for a system for advance booking for highway use," Transport Policy, Elsevier, vol. 4(2), pages 109-114, April.

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