Networks and Spatial Economics

, Volume 10, Issue 4, pp 525–550 | Cite as

Integrated Network Capacity Expansion and Traffic Signal Optimization Problem: Robust Bi-level Dynamic Formulation



This paper presents a robust optimization formulation, with an exact solution method, that simultaneously solves continuous network capacity expansion, traffic signal optimization and dynamic traffic assignment when explicitly accounting for an appropriate robustness measure, the inherent bi-level nature of the problem and long-term O-D demand uncertainty. The adopted robustness measure is the weighted sum of expected total system travel time (TSTT) and squared up-side deviation from a fixed target. The model propagates traffic according to Daganzo’s cell transmission model. Furthermore, we formulate five additional, related models. We find that when evaluated in terms of robustness, the integrated robust model performs the best, and interestingly the sequential robust approach yields a worse solution compared to certain sequential and integrated approaches. Although the adopted objective of the integrated robust model does not directly optimize the variance of TSTT, our experimental results show that the robust solutions also yield the least-variance solutions.


Network design problem Signal optimization Dynamic traffic assignment Robust optimization Bilevel programming 



This research is based on work supported by the National Science Foundation (NSF) project “CAREER: Accounting for Information and Recourse in the Robust Design and Optimization of Stochastic Transportation Networks” and the Southwest Region University Transportation Center (SWUTC) project SWUTC/06/167867 “Multimodal Network Models for Robust Transportation Systems.” The work presented in this paper remains the sole responsibility of the authors.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.School of Transportation EngineeringSuranaree University of TechnologyNakhon RatchasimaThailand
  2. 2.Department of Civil, Architectural and Environmental EngineeringThe University of Texas at AustinAustinUSA

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