Flow Breakdown, Travel Reliability and Real-time Information in Route Choice Behavior

  • Jing Dong
  • Hani S. Mahmassani


The objective of this study is to provide travelers in a congested network with information on the relative reliability of alternative travel routes, in addition to the usual travel time information. Towards this end, we develop a travel reliability measure that captures the probability of flow breakdown along a given facility, along with the conditional expected delay associated with occurrence of breakdown. The paper develops a methodology for estimating the key elements of this measure, and provides an empirical realization using commonly available freeway sensor data. Both elements of the reliability measure, namely the probability of flow breakdown and the extra delay caused by breakdown are represented as functions of flow rate, and calibrated for each road section based on field data. The proposed travel reliability measure could therefore be obtained off-line by analyzing historical data and computed on-line when real-time measurements are available. The reliability measure is incorporated in the generalized cost function underlying drivers’ route choice behavior, as a basis for dynamic traffic assignment under reliability information provision to users. An analytical illustration using an idealized two route network is provided, confirming that reliability information could improve system performance and increase overall social welfare. Application of the approach to the Irvine, CA test network provides a real-network assessment of the value of travel reliability information in the context of real-time traveler information provision. The experimental results show that reliability information helps to relieve congestion on the freeway, increase system utilization and reduce travelers’ trip time.


Travel Time Traffic Flow Route Choice Transportation Research Record Transportation Research Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag US 2009

Authors and Affiliations

  • Jing Dong
    • 1
  • Hani S. Mahmassani
    • 1
  1. 1.Northwestern UniversityCaliforniaU.S.A

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