IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v52y2018i4d10.1007_s10614-017-9790-5.html

Environmental Protection in Scenic Areas: Traffic Scheme for Clean Energy Vehicles Based on Multi-agent

Author

Listed:
  • Lei Li

    (Tianjin University)

  • Wenting Liu

    (Tianjin University)

  • Lindi Xiao

    (Tianjin University)

  • Hui Sun

    (Tianjin University)

  • Shi Wang

    (Carnegie Mellon University)

Abstract

The low-carbon environmental protection and traffic congestion are two primary issues that people focus on highly and need to be solved efficiently. Under the circumstance, this paper designs a transportation simulation system for clean energy vehicles in scenic area based on multi-agent. From the view of introducing clean energy electric vehicles with limited funds and unlimited funds, we investigate the optimal introduction scheme and the optimal traffic scheme of clean energy vehicles to alleviate air pollution and tourist overcrowding. Combined with the specific circumstances of a famous scenic spot in China, we conduct the simulation and propose many countermeasures and suggestions to improve traffic scheme of clean energy vehicles.

Suggested Citation

  • Lei Li & Wenting Liu & Lindi Xiao & Hui Sun & Shi Wang, 2018. "Environmental Protection in Scenic Areas: Traffic Scheme for Clean Energy Vehicles Based on Multi-agent," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1069-1087, December.
    • Handle: RePEc:kap:compec:v:52:y:2018:i:4:d:10.1007_s10614-017-9790-5
    DOI: 10.1007/s10614-017-9790-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-017-9790-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-017-9790-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. He, Xiaozheng & Guo, Xiaolei & Liu, Henry X., 2010. "A link-based day-to-day traffic assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 597-608, May.
    2. Tian, Qiong & Huang, Hai-Jun & Yang, Hai, 2007. "Equilibrium properties of the morning peak-period commuting in a many-to-one mass transit system," Transportation Research Part B: Methodological, Elsevier, vol. 41(6), pages 616-631, July.
    3. Angelo Antoci & Marcello Galeotti & Davide Radi, 2011. "Financial Tools for the Abatement of Traffic Congestion: A Dynamical Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 38(3), pages 389-405, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Malin Song & Ron Fisher, 2018. "How to Apply Advanced Statistical Analysis to Computational Economics: Methods and Insights," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1045-1052, December.

    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. Liu, Peng & Liao, Feixiong & Tian, Qiong & Huang, Hai-Jun & Timmermans, Harry, 2020. "Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 208-227.
    2. 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.
    3. Arnott, Richard & DePalma, Elijah, 2011. "The corridor problem: Preliminary results on the no-toll equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 743-768, June.
    4. Wen-yi Zhang & Wei Guan & Ji-hui Ma & Jun-fang Tian, 2015. "A Nonlinear Pairwise Swapping Dynamics to Model the Selfish Rerouting Evolutionary Game," Networks and Spatial Economics, Springer, vol. 15(4), pages 1075-1092, December.
    5. Han, Linghui & Wang, David Z.W. & Lo, Hong K. & Zhu, Chengjuan & Cai, Xingju, 2017. "Discrete-time day-to-day dynamic congestion pricing scheme considering multiple equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 1-16.
    6. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    7. Wang, Wei (Walker) & Wang, David Z.W. & Zhang, Fangni & Sun, Huijun & Zhang, Wenyi & Wu, Jianjun, 2017. "Overcoming the Downs-Thomson Paradox by transit subsidy policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 126-147.
    8. 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).
    9. 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.
    10. Ren-Yong Guo & Hai-Jun Huang & Hai Yang, 2019. "Tradable Credit Scheme for Control of Evolutionary Traffic Flows to System Optimum: Model and its Convergence," Networks and Spatial Economics, Springer, vol. 19(3), pages 833-868, September.
    11. Angelo Antoci & Marcello Galeotti & Davide Radi, 2011. "Financial Tools for the Abatement of Traffic Congestion: A Dynamical Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 38(3), pages 389-405, October.
    12. Liang, Zhiyuan & Tang, Yili & Yu, Jianing & Wang, Yacan, 2024. "A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    13. Liu, Peng & Liu, Jielun & Ong, Ghim Ping & Tian, Qiong, 2020. "Flow pattern and optimal capacity in a bi-modal traffic corridor with heterogeneous users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    14. Minyu Shen & Feng Xiao & Weihua Gu & Hongbo Ye, 2024. "Cognitive Hierarchy in Day-to-day Network Flow Dynamics," Papers 2409.11908, arXiv.org, revised Jun 2025.
    15. Zhang, Junlin & Yang, Hai & Lindsey, Robin & Li, Xinwei, 2020. "Modeling and managing congested transit service with heterogeneous users under monopoly," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 249-266.
    16. Cabo, Francisco & García-González, Ana, 2020. "Interaction and imitation with heterogeneous agents: A misleading evolutionary equilibrium," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 152-174.
    17. Qi, Hang & Jia, Ning & Qu, Xiaobo & He, Zhengbing, 2023. "Investigating day-to-day route choices based on multi-scenario laboratory experiments, Part I: Route-dependent attraction and its modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    18. Han, Linghui & Sun, Huijun & Wu, Jianjun & Zhu, Chengjuan, 2011. "Day-to-day evolution of the traffic network with Advanced Traveler Information System," Chaos, Solitons & Fractals, Elsevier, vol. 44(10), pages 914-919.
    19. Toru Seo & Kentaro Wada & Daisuke Fukuda, 2023. "Fundamental diagram of urban rail transit considering train–passenger interaction," Transportation, Springer, vol. 50(4), pages 1399-1424, August.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:kap:compec:v:52:y:2018:i:4:d:10.1007_s10614-017-9790-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.