Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, the authors review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, the authors identify challenges in applying behavioral route choice models to NDP and opportunities for future research.

Language

  • English

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Filing Info

  • Accession Number: 01600767
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 25 2016 3:01PM