IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v149y2021icp138-161.html
   My bibliography  Save this article

Incentive-based decentralized routing for connected and autonomous vehicles using information propagation

Author

Listed:
  • Wang, Chaojie
  • Peeta, Srinivas
  • Wang, Jian

Abstract

Routing strategies under the aegis of dynamic traffic assignment have been proposed in the literature to optimize system performance. However, challenges have persisted in their deployment ability and effectiveness due to inherent strong assumptions on traveler behavior and availability of network-level real-time traffic information, and the high computational burden associated with computing network-wide flows in real-time. To address these gaps, this study proposes an incentive-based decentralized routing strategy to nudge the network performance closer to the system optimum for the context where all vehicles are connected and autonomous vehicles (CAVs). The strategy consists of three stages. The first stage incorporates a local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles’ knowledge of local traffic information. This system is decentralized in the sense that it only updates the local route choices of vehicles in this area rather than route choices of all vehicles in the network, which circumvents the high computational burden associated with computing the flows on the entire network. The second stage optimizes the route for each CAV by considering individual heterogeneity in traveler preferences (e.g., the value of time) to maximize the utilities of all travelers in the local area. Constraints are also incorporated to ensure that these routes can achieve the approximated local system optimal flow of the first stage. The third stage leverages an expected envy-free incentive mechanism to ensure that travelers in the local area can accept the optimal routes determined in the second stage. The study analytically discusses the convergence of the local route switching dynamical system. We also show that the proposed incentive mechanism is expected individual rational and budget-balanced, which ensure that travelers are willing to participate and guarantee the balance between payments and compensations, respectively. Further, the conditions for the expected incentive compatibility of the incentive mechanism are analyzed and proved, ensuring behavioral honesty in disclosing information. Thereby, the proposed incentive-based decentralized routing strategy can enhance network performance and user satisfaction under fully connected and autonomous environments.

Suggested Citation

  • Wang, Chaojie & Peeta, Srinivas & Wang, Jian, 2021. "Incentive-based decentralized routing for connected and autonomous vehicles using information propagation," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 138-161.
    • Handle: RePEc:eee:transb:v:149:y:2021:i:c:p:138-161
    DOI: 10.1016/j.trb.2021.05.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261521000837
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2021.05.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. , & , & ,, 2014. "Budget-balance, fairness and minimal manipulability," Theoretical Economics, Econometric Society, vol. 9(3), September.
    2. Sun, Ning & Yang, Zaifu, 2003. "A general strategy proof fair allocation mechanism," Economics Letters, Elsevier, vol. 81(1), pages 73-79, October.
    3. Claus-Jochen Haake & Matthias G. Raith & Francis Edward Su, 2002. "Bidding for envy-freeness: A procedural approach to n-player fair-division problems," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 19(4), pages 723-749.
    4. Wang, Jian & Peeta, Srinivas & Lu, Lili & Li, Tao, 2019. "Multiclass information flow propagation control under vehicle-to-vehicle communication environments," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 96-121.
    5. Michael J. Smith, 1984. "The Stability of a Dynamic Model of Traffic Assignment---An Application of a Method of Lyapunov," Transportation Science, INFORMS, vol. 18(3), pages 245-252, August.
    6. Richard Mounce & Malachy Carey, 2015. "On the Convergence of the Method of Successive Averages for Calculating Equilibrium in Traffic Networks," Transportation Science, INFORMS, vol. 49(3), pages 535-542, August.
    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. , & , & ,, 2014. "Budget-balance, fairness and minimal manipulability," Theoretical Economics, Econometric Society, vol. 9(3), September.
    2. Andersson, Tommy & Ehlers, Lars & Svensson, Lars-Gunnar, 2014. "Least manipulable Envy-free rules in economies with indivisibilities," Mathematical Social Sciences, Elsevier, vol. 69(C), pages 43-49.
    3. ANDERSSON, Tommy & EHLERS, Lars & SVENSSON, Lars-Gunnar, 2012. "(Minimally) 'epsilon'-Incentive Compatible Competitive Equilibria in Economies with Indivisibilities," Cahiers de recherche 2012-03, Universite de Montreal, Departement de sciences economiques.
    4. Tommy Andersson & Lars Ehlers & Lars-Gunnar Svensson, 2012. "(Minimally) ?-Incentive Compatible Competitive Equilibria in Economies with Indivisibilities," Cahiers de recherche 04-2012, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Tommy ANDERSSON & Lars EHLERS, 2013. "An Algorithm for Identifying Agent-k-Linked Allocations in Economies with Indivisibilities," Cahiers de recherche 17-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    6. Tommy Andersson & Christer Andersson, 2009. "Solving House Allocation Problems with Risk-Averse Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(4), pages 389-401, May.
    7. David Pérez-Castrillo & Marilda Sotomayor, 2017. "On the manipulability of competitive equilibrium rules in many-to-many buyer–seller markets," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(4), pages 1137-1161, November.
    8. Ning Sun & Zaifu Yang, 2009. "Strategy Proof And Privacy Preserving Fair Allocation Mechanism," The Japanese Economic Review, Japanese Economic Association, vol. 60(2), pages 143-151, June.
    9. Lie Han, 2022. "Proportional-Switch Adjustment Process with Elastic Demand and Congestion Toll in the Absence of Demand Functions," Networks and Spatial Economics, Springer, vol. 22(4), pages 709-735, December.
    10. Wang, Dong & Liao, Feixiong & Gao, Ziyou & Timmermans, Harry, 2019. "Tolerance-based strategies for extending the column generation algorithm to the bounded rational dynamic user equilibrium problem," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 102-121.
    11. Qixiu Cheng & Zhiyuan Liu & Feifei Liu & Ruo Jia, 2017. "Urban dynamic congestion pricing: an overview and emerging research needs," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 3-18, August.
    12. Tommy Andersson & Lars Ehlers & Lars-Gunnar Svensson & Ryan Tierney, 2022. "Gale’s Fixed Tax for Exchanging Houses," Mathematics of Operations Research, INFORMS, vol. 47(4), pages 3110-3128, November.
    13. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    14. Richard Mounce, 2007. "Convergence to Equilibrium in Dynamic Traffic Networks when Route Cost Is Decay Monotone," Transportation Science, INFORMS, vol. 41(3), pages 409-504, August.
    15. Iryo, Takamasa & Watling, David, 2019. "Properties of equilibria in transport problems with complex interactions between users," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 87-114.
    16. Peeta, Srinivas, 2016. "A marginal utility day-to-day traffic evolution model based on one-step strategic thinkingAuthor-Name: He, Xiaozheng," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 237-255.
    17. Ehtamo, Harri & Kettunen, Eero & Hamalainen, Raimo P., 2001. "Searching for joint gains in multi-party negotiations," European Journal of Operational Research, Elsevier, vol. 130(1), pages 54-69, April.
    18. Yu Zhou & Shigehiro Serizawa, 2019. "Minimum price equilibrium in the assignment market," ISER Discussion Paper 1047, Institute of Social and Economic Research, Osaka University.
    19. Alfredo Valencia-Toledo & Juan Vidal-Puga, 2020. "Reassignment-proof rules for land rental problems," International Journal of Game Theory, Springer;Game Theory Society, vol. 49(1), pages 173-193, March.
    20. Zhang, Ding & Nagurney, Anna & Wu, Jiahao, 2001. "On the equivalence between stationary link flow patterns and traffic network equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 35(8), pages 731-748, September.

    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:eee:transb:v:149:y:2021:i:c:p:138-161. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.