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Evaluation of diversion strategies using dynamic traffic assignment

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  • Constantinos Antoniou
  • Haris N. Koutsopoulos
  • Moshe Ben-Akiva
  • Akhilendra S. Chauhan

Abstract

A framework for the evaluation of the effectiveness of traffic diversion strategies for non-recurrent congestion, based on predictive guidance and using dynamic traffic assignment, is presented. Predictive guidance is based on a short-term prediction of traffic conditions, incorporating user reaction to information and guidance. A case study of the Lower Westchester County network in New York State, using DynaMIT-P, is presented to illustrate the application of the framework. DynaMIT-P is capable of evaluating diversion strategies based on predicted conditions, which take into account drivers’ response to traffic information. The case study simulates the operations of predictive variable message signs positioned in strategic locations. DynaMIT-P is calibrated for the study network and used to establish base conditions for two incident scenarios in the absence of advanced traveller information systems. The effectiveness of predictive diversion strategies is evaluated (using rigorous statistical tests) by comparing traffic conditions with and without diversion strategies. The empirical findings suggest that incident diversion strategies based on predictive guidance result in travel time savings and increased travel time reliability.

Suggested Citation

  • Constantinos Antoniou & Haris N. Koutsopoulos & Moshe Ben-Akiva & Akhilendra S. Chauhan, 2011. "Evaluation of diversion strategies using dynamic traffic assignment," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(3), pages 199-216, February.
  • Handle: RePEc:taf:transp:v:34:y:2011:i:3:p:199-216
    DOI: 10.1080/03081060.2011.565168
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    Cited by:

    1. Xie, Jiaohong & Yang, Zhenyu & Lai, Xiongfei & Liu, Yang & Yang, Xiao Bo & Teng, Teck-Hou & Tham, Chen-Khong, 2022. "Deep reinforcement learning for dynamic incident-responsive traffic information dissemination," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).

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