IDEAS home Printed from https://ideas.repec.org/p/eti/dpaper/25055.html
   My bibliography  Save this paper

Quantifying Congestion Externalities in Road Networks: A structural estimation approach using stochastic evolutionary model

Author

Listed:
  • Shota FUJISHIMA
  • Takara SAKAI
  • Yuki TAKAYAMA

Abstract

This study estimates the structural parameters of a travel time function, which relates traffic volume to travel time, within the context of a traffic assignment model in which travelers strategically select routes to minimize their travel costs, influenced by congestion. The proposed model is formulated as a potential game, enabling the estimation of parameters using the maximum likelihood method based on a stochastic evolutionary process. The impact of congestion pricing on welfare is evaluated using the estimated parameters. Preliminary analysis using the Sioux Falls network shows that congestion pricing enhances overall welfare, even when accounting for estimation errors.

Suggested Citation

  • Shota FUJISHIMA & Takara SAKAI & Yuki TAKAYAMA, 2025. "Quantifying Congestion Externalities in Road Networks: A structural estimation approach using stochastic evolutionary model," Discussion papers 25055, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:25055
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/25e055.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Angelo Mele, 2017. "A Structural Model of Dense Network Formation," Econometrica, Econometric Society, vol. 85, pages 825-850, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zenou, Yves & Boucher, Vincent & Tumen, Semih & Vlassopoulos, Michael & Wahba, Jackline, 2020. "Ethnic Mixing in Early Childhood: Evidence from a Randomized Field Experiment and a Structural Model," CEPR Discussion Papers 15528, C.E.P.R. Discussion Papers.
    2. Guanyi Wang, 2024. "Robust Network Targeting with Multiple Nash Equilibria," Papers 2410.20860, arXiv.org, revised Nov 2024.
    3. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    4. Gaonkar, Shweta & Mele, Angelo, 2023. "A model of inter-organizational network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 82-104.
    5. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    6. Chen, Xi & Qiu, Yun & Shi, Wei & Yu, Pei, 2022. "Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic," China Economic Review, Elsevier, vol. 73(C).
    7. Monica Billio & Roberto Casarin & Matteo Iacopini, 2024. "Bayesian Markov-Switching Tensor Regression for Time-Varying Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 109-121, January.
    8. Abhijit Banerjee & Emily Breza & Arun G Chandrasekhar & Esther Duflo & Matthew O Jackson & Cynthia Kinnan, 2024. "Changes in Social Network Structure in Response to Exposure to Formal Credit Markets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1331-1372.
    9. Calvano, Emilio & Immordino, Giovanni & Scognamiglio, Annalisa, 2022. "What drives segregation? Evidence from social interactions among students," Economics of Education Review, Elsevier, vol. 90(C).
    10. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    11. Shujie Ma & Liangjun Su & Yichong Zhang, 2020. "Detecting Latent Communities in Network Formation Models," Economics and Statistics Working Papers 12-2020, Singapore Management University, School of Economics.
    12. Gao, Wayne Yuan & Li, Ming & Xu, Sheng, 2023. "Logical differencing in dyadic network formation models with nontransferable utilities," Journal of Econometrics, Elsevier, vol. 235(1), pages 302-324.
    13. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    14. Merlino, Luca Paolo & Steinhardt, Max Friedrich & Wren-Lewis, Liam, 2024. "The long run impact of childhood interracial contact on residential segregation," Journal of Public Economics, Elsevier, vol. 239(C).
    15. Áureo de Paula, 2020. "Econometric Models of Network Formation," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 775-799, August.
    16. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    17. Yoshiyuki ARATA & Philipp MUNDT, 2019. "Topology and Formation of Production Input Interlinkages: Evidence from Japanese microdata," Discussion papers 19027, Research Institute of Economy, Trade and Industry (RIETI).
    18. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    19. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    20. Balbus, Lukasz & Dziewulski, Pawel & Reffett, Kevin & Wozny, Lukasz, 2022. "Markov distributional equilibrium dynamics in games with complementarities and no aggregate risk," Theoretical Economics, Econometric Society, vol. 17(2), May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eti:dpaper:25055. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.