Robust Transportation Network Design Under Demand Uncertainty

This paper addresses the topic of traffic network design problems (NDP) under demand uncertainty. The origin–destination trip matrices are taken as random variables with known probability distributions. Instead of finding optimal network design solutions for a given future scenario, the authors focus on solutions helpful for a variety of demand realizations. A definition of robustness accounting for the planner's required degree of robustness is introduced, a formulation of the robust network design problem (RNDP) is proposed, and a methodology based on genetic algorithms (GA) to solve the RNDP is developed. The proposed model generates globally near-optimal network design solutions, f, based on the planner's input for robustness. The study makes 2 key contributions to the network design literature. First, robust network design solutions are significantly different from deterministic NDPs and not accounting for them could potentially underestimate the network-wide impacts. Second, systematic evaluation of the performance of the model and solution algorithm is conducted on different test networks and budget levels to explore the efficacy of this approach. Results highlight the importance of accounting for robustness in transportation planning and that the proposed approach is capable of producing high-quality solutions.

  • Availability:
  • Authors:
    • Ukkusuri, Satish V
    • Mathew, Tom V
    • Waller, S Travis
  • Publication Date: 2007-1

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01042261
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 1 2007 8:37AM