Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities
network design, behavior route choice, random utility, random regret, bounded rationality, cumulative prospect theory, fuzzy logic, dynamic learning, SILK theory
Digital Object Identifier (DOI)
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, we 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, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.
Was this content written or created while at USF?
Citation / Publisher Attribution
Transport Reviews, v. 36, issue 4, p. 454-478
Scholar Commons Citation
Sun, Longsheng; Karwan, Mark H.; and Kwon, Changhyun, "Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities" (2016). Industrial and Management Systems Engineering Faculty Publications. 6.