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The Effect of Reference Point on Stochastic Network Equilibrium

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  • Erel Avineri

    (Centre for Transport and Society, Faculty of the Built Environment, University of the West of England, Bristol, United Kingdom)

Abstract

Following studies of human decision making under risk and uncertainty, an extensive evidence of loss aversion and asymmetric risk-taking behavior around a reference point was found. Prospect theory proposes an alternative framework to the traditional risk-taking modeling in travel behavior, which might be too simplistic. This paper examines the possibility of applying prospect theory for modeling stochastic network equilibrium, and presents an investigation of the effect of reference point value on such equilibrium. Conceptual and methodological issues that could be addressed by further research in transportation research are suggested.

Suggested Citation

  • Erel Avineri, 2006. "The Effect of Reference Point on Stochastic Network Equilibrium," Transportation Science, INFORMS, vol. 40(4), pages 409-420, November.
    • Handle: RePEc:inm:ortrsc:v:40:y:2006:i:4:p:409-420
    DOI: 10.1287/trsc.1060.0158
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    2. Dixit, Vinayak V. & Harb, Rami C. & Martínez-Correa, Jimmy & Rutström, Elisabet E., 2015. "Measuring risk aversion to guide transportation policy: Contexts, incentives, and respondents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 15-34.
    3. Prateek Bansal & Daniel Horcher & Daniel J. Graham, 2020. "A Dynamic Choice Model with Heterogeneous Decision Rules: Application in Estimating the User Cost of Rail Crowding," Papers 2007.03682, arXiv.org.
    4. Wei, Fangfang & Jia, Ning & Ma, Shoufeng, 2016. "Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 335-354.
    5. Xie, Chi & Liu, Zugang, 2014. "On the stochastic network equilibrium with heterogeneous choice inertia," Transportation Research Part B: Methodological, Elsevier, vol. 66(C), pages 90-109.
    6. Fei Han & Jian Wang & Lingli Huang & Yan Li & Liu He, 2023. "Modeling Impacts of Implementation Policies of Tradable Credit Schemes on Traffic Congestion in the Context of Traveler’s Cognitive Illusion," Sustainability, MDPI, vol. 15(15), pages 1-18, July.
    7. Xu, Hongli & Lou, Yingyan & Yin, Yafeng & Zhou, Jing, 2011. "A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 311-328, February.
    8. Xiangfeng Ji & Xuegang (Jeff) Ban & Mengtian Li & Jian Zhang & Bin Ran, 2017. "Non-expected Route Choice Model under Risk on Stochastic Traffic Networks," Networks and Spatial Economics, Springer, vol. 17(3), pages 777-807, September.
    9. Xinming Zang & Zhenqi Guo & Jingai Ma & Yongguang Zhong & Xiangfeng Ji, 2021. "Target-Oriented User Equilibrium Considering Travel Time, Late Arrival Penalty, and Travel Cost on the Stochastic Tolled Traffic Network," Sustainability, MDPI, vol. 13(17), pages 1-22, September.
    10. Jou, Rong-Chang & Chen, Ke-Hong, 2013. "An application of cumulative prospect theory to freeway drivers’ route choice behaviours," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 123-131.
    11. Metcalfe, Robert & Dolan, Paul, 2012. "Behavioural economics and its implications for transport," Journal of Transport Geography, Elsevier, vol. 24(C), pages 503-511.
    12. Chao Huang & Mark Burris & W. Douglass Shaw, 2017. "Differences in probability weighting for individual travelers: a managed lane choice application," Transportation, Springer, vol. 44(2), pages 375-393, March.
    13. Hongli Xu & Hai Yang & Jing Zhou & Yafeng Yin, 2017. "A Route Choice Model with Context-Dependent Value of Time," Transportation Science, INFORMS, vol. 51(2), pages 536-548, May.
    14. Andrea C. Hupman & Jay Simon, 2023. "The Legacy of Peter Fishburn: Foundational Work and Lasting Impact," Decision Analysis, INFORMS, vol. 20(1), pages 1-15, March.
    15. Avineri, Erel, 2012. "On the use and potential of behavioural economics from the perspective of transport and climate change," Journal of Transport Geography, Elsevier, vol. 24(C), pages 512-521.
    16. Connors, Richard D. & Sumalee, Agachai, 2009. "A network equilibrium model with travellers' perception of stochastic travel times," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 614-624, July.
    17. Bao, Yue & Gao, Ziyou & Xu, Meng & Sun, Huijun & Yang, Hai, 2015. "Travel mental budgeting under road toll: An investigation based on user equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 1-17.
    18. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    19. Guang Yang & Xinwang Liu, 2018. "A commuter departure-time model based on cumulative prospect theory," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(2), pages 285-307, April.
    20. Giselle Moraes Ramos & Winnie Daamen & Serge Hoogendoorn, 2014. "A State-of-the-Art Review: Developments in Utility Theory, Prospect Theory and Regret Theory to Investigate Travellers' Behaviour in Situations Involving Travel Time Uncertainty," Transport Reviews, Taylor & Francis Journals, vol. 34(1), pages 46-67, January.

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