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Risk aversion, the value of information and traffic equilibrium

Author

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  • André de Palma

    (X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique, ENS Cachan - École normale supérieure - Cachan)

  • Robin Lindsey

    (University of Alberta)

  • Nathalie Picard

    (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique)

Abstract

Information about traffic conditions has traditionally been conveyed to drivers by radio and variable message signs, and more recently via the Internet and Advanced Traveler Information Systems. This has spurred research on how travelers respond to information, how much they are willing to pay for it and how much they are likely to benefit from it collectively. In this paper we analyze the decisions of drivers whether to acquire information and which route to take on a simple congested road network. Drivers vary in their degree of risk aversion with respect to travel time. Four information regimes are considered: No information, Free information which is publicly available at no cost, Costly information which is publicly available for a fee, and Private information which is available free to a single individual. Private information is shown to be individually more valuable than either Free or Costly information, while the benefits from Free and Costly information cannot be ranked in general. Free or Costly information can decrease the expected utility of drivers who are very risk-averse, and with sufficient risk aversion in the population the aggregate compensating variation for information can be negative.

Suggested Citation

  • André de Palma & Robin Lindsey & Nathalie Picard, 2008. "Risk aversion, the value of information and traffic equilibrium," Working Papers hal-00349492, HAL.
  • Handle: RePEc:hal:wpaper:hal-00349492
    Note: View the original document on HAL open archive server: https://hal.science/hal-00349492
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    References listed on IDEAS

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    Cited by:

    1. Hoang, Nam H. & Vu, Hai L. & Lo, Hong K., 2018. "An informed user equilibrium dynamic traffic assignment problem in a multiple origin-destination stochastic network," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 207-230.
    2. Engelson, Leonid & Fosgerau, Mogens, 2020. "Scheduling preferences and the value of travel time information," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 256-265.
    3. Zhaoqi Zang & Richard Batley & Xiangdong Xu & David Z. W. Wang, 2022. "On the value of distribution tail in the valuation of travel time variability," Papers 2207.06293, arXiv.org, revised Dec 2023.
    4. Fosgerau, Mogens & Lindsey, Robin, 2013. "Trip-timing decisions with traffic incidents," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 764-782.
    5. Yuan, PengCheng & Juan, ZhiCai, 2013. "The related congestion failure estimating methodology and model in transportation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4330-4344.
    6. André De Palma & Nathalie Picard & Matthieu De Lapparent, 2014. "Risky Time Prospects and Travel Demand," Mathematical Population Studies, Taylor & Francis Journals, vol. 21(4), pages 185-188, December.
    7. Piet Rietveld, 2011. "The Economics of Information in Transport," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 24, Edward Elgar Publishing.
    8. Yu, Xiaojuan & van den Berg, Vincent A.C. & Li, Zhi-Chun, 2023. "Congestion pricing and information provision under uncertainty: Responsive versus habitual pricing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    9. Marie Schmidt & Leo Kroon & Anita Schöbel & Paul Bouman, 2017. "The Travelers Route Choice Problem Under Uncertainty: Dominance Relations Between Strategies," Operations Research, INFORMS, vol. 65(1), pages 184-199, February.
    10. 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.
    11. Lindsey, Robin & Daniel, Terry & Gisches, Eyran & Rapoport, Amnon, 2014. "Pre-trip information and route-choice decisions with stochastic travel conditions: Theory," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 187-207.
    12. Han, Xiao & Yu, Yun & Gao, Zi-You & Zhang, H. Michael, 2021. "The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 205-226.
    13. Bifulco, Gennaro N. & Cantarella, Giulio E. & Simonelli, Fulvio & Velonà, Pietro, 2016. "Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 73-87.
    14. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    15. Marie Schmidt & Leo Kroon & Anita Schöbel & Paul Bouman, 2017. "The Travelers Route Choice Problem Under Uncertainty: Dominance Relations Between Strategies," Operations Research, INFORMS, vol. 65(1), pages 184-199, February.
    16. Wijayaratna, Kasun P. & Dixit, Vinayak V., 2016. "Impact of information on risk attitudes: Implications on valuation of reliability and information," Journal of choice modelling, Elsevier, vol. 20(C), pages 16-34.
    17. Rapoport, Amnon & Gisches, Eyran J. & Daniel, Terry & Lindsey, Robin, 2014. "Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 154-172.

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    Keywords

    Transportation; route choice; information provision; expected utility; congestion;
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