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Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

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  • Longsheng Sun
  • Mark H. Karwan
  • Changhyun Kwon

Abstract

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.

Suggested Citation

  • Longsheng Sun & Mark H. Karwan & Changhyun Kwon, 2016. "Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities," Transport Reviews, Taylor & Francis Journals, vol. 36(4), pages 454-478, July.
    • Handle: RePEc:taf:transr:v:36:y:2016:i:4:p:454-478
    DOI: 10.1080/01441647.2015.1091047
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    Cited by:

    1. Lattarulo, Patrizia & Masucci, Valentino & Pazienza, Maria Grazia, 2019. "Resistance to change: Car use and routines," Transport Policy, Elsevier, vol. 74(C), pages 63-72.
    2. Ali Keyvanfar & Arezou Shafaghat & Nasiru Zakari Muhammad & M. Salim Ferwati, 2018. "Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    3. Duan, Liwei & Tavasszy, Lorant A. & Rezaei, Jafar, 2019. "Freight service network design with heterogeneous preferences for transport time and reliability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 124(C), pages 1-12.
    4. Xu, Xiangdong & Chen, Anthony & Xu, Guangming & Yang, Chao & Lam, William H.K., 2021. "Enhancing network resilience by adding redundancy to road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    5. Manley, Ed & Cheng, Tao, 2018. "Exploring the role of spatial cognition in predicting urban traffic flow through agent-based modelling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 14-23.
    6. Eikenbroek, Oskar A.L. & Still, Georg J. & van Berkum, Eric C. & Kern, Walter, 2018. "The Boundedly Rational User Equilibrium: A parametric analysis with application to the Network Design Problem," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 1-17.
    7. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.

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