Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing the total travel cost 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, we review the NDP with various route choice models: the random utility model (RUM), bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and search-information-learning-knowledge (SILK) theory. Additionally, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.