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Networks and Spatial Economics

, Volume 12, Issue 1, pp 167–181 | Cite as

Dynamic Traffic Assignment under Uncertainty: A Distributional Robust Chance-Constrained Approach

  • Byung Do Chung
  • Tao Yao
  • Bo Zhang
Article

Abstract

This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.

Keywords

Dynamic traffic assignment Transportation planning Chance-constrained programming Joint chance constraint Data uncertainty 

Notes

Acknowledgment

This work was partially supported by the grant awards CMMI-0824640 and CMMI-0900040 from the National Science Foundation and the grant awards from the Mid-Atlantic Universities Transportation Center (MAUTC).

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Industrial and Manufacturing EngineeringThe Pennsylvania State UniversityUniversity ParkUSA

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