IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11521-d914579.html
   My bibliography  Save this article

A Dynamic Road Network Model for Coupling Simulation of Highway Infrastructure Performance and Traffic State

Author

Listed:
  • Zhen Yang

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Ruiping Zheng

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Gang Wang

    (Highway Monitoring and Response Center, Ministry of Transport, Beijing 100029, China)

  • Kefu Zhou

    (Highway Monitoring and Response Center, Ministry of Transport, Beijing 100029, China)

Abstract

The state of the road network contains both the infrastructure performance and the traffic operation state of the road network. There is a strong coupling between the decay of the infrastructure performance and the redistribution of the traffic flow on the road network. In this paper, a dynamic road network description model is proposed to apply to the couple simulation of highway network infrastructure performance and traffic state. First, a road network description model is constructed by associating the highway network topology with state attributes. The topology contains traffic information and is dynamically editable. Then, a dynamic road network model is proposed that can dynamically represent the changes in local connectivity relationships caused by traffic control, such as lane/ramp closures and turning restrictions in actual roads due to construction operations and access to the state of multi-scale spatio-temporal road networks. It overcomes the defects of the existing road network model, which is difficult to apply to the analysis of service performance and traffic state of the road network in different periods. Finally, the application of the dynamic road network model in the highway network coupled simulation system (HNCS) is completed, which provides a method for improving the efficiency and accuracy of large-scale highway network traffic simulation and highway infrastructure performance prediction.

Suggested Citation

  • Zhen Yang & Ruiping Zheng & Gang Wang & Kefu Zhou, 2022. "A Dynamic Road Network Model for Coupling Simulation of Highway Infrastructure Performance and Traffic State," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11521-:d:914579
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11521/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/18/11521/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ling Zheng & Bijun Li & Bo Yang & Huashan Song & Zhi Lu, 2019. "Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    2. Sang Nguyen & Clermont Dupuis, 1984. "An Efficient Method for Computing Traffic Equilibria in Networks with Asymmetric Transportation Costs," Transportation Science, INFORMS, vol. 18(2), pages 185-202, 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. Lingyu Zhang & Li Wang & Lili Zhang & Xiao Zhang & Dehui Sun, 2023. "An RSU Deployment Scheme for Vehicle-Infrastructure Cooperated Autonomous Driving," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    2. D.R. Han & H.K. Lo, 2002. "New Alternating Direction Method for a Class of Nonlinear Variational Inequality Problems," Journal of Optimization Theory and Applications, Springer, vol. 112(3), pages 549-560, March.
    3. Ng, ManWo & Waller, S. Travis, 2010. "A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1202-1219, December.
    4. Szeto, W. Y. & Lo, Hong K., 2004. "A cell-based simultaneous route and departure time choice model with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 593-612, August.
    5. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    6. Zhu, Feng & Ukkusuri, Satish V., 2017. "Efficient and fair system states in dynamic transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 272-289.
    7. Huang, Ruqing & Han, Lee D. & Huang, Zhongxiang, 2022. "A new network equilibrium flow model: User-equilibrium with quantity adjustment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    8. Lundgren, Jan T. & Peterson, Anders, 2008. "A heuristic for the bilevel origin-destination-matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 339-354, May.
    9. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    10. Li, Tongfei & Xu, Min & Sun, Huijun & Xiong, Jie & Dou, Xueping, 2023. "Stochastic ridesharing equilibrium problem with compensation optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    11. Zarbakhshnia, Navid & Ma, Zhenliang, 2024. "Critical success factors for the adoption of AVs in sustainable urban transportation," Transport Policy, Elsevier, vol. 156(C), pages 62-76.
    12. Sun, Mingmei, 2023. "A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    13. Hongbo Ye & Hai Yang, 2017. "Rational Behavior Adjustment Process with Boundedly Rational User Equilibrium," Transportation Science, INFORMS, vol. 51(3), pages 968-980, August.
    14. Elnaz Miandoabchi & Reza Farahani & W. Szeto, 2012. "Bi-objective bimodal urban road network design using hybrid metaheuristics," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 583-621, December.
    15. Jiancheng Long & Hai-Jun Huang & Ziyou Gao & W. Y. Szeto, 2013. "An Intersection-Movement-Based Dynamic User Optimal Route Choice Problem," Operations Research, INFORMS, vol. 61(5), pages 1134-1147, October.
    16. Tan, Zhijia & Yang, Hai & Tan, Wei & Li, Zhichun, 2016. "Pareto-improving transportation network design and ownership regimes," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 292-309.
    17. Liu, Haoxiang & Wang, David Z.W., 2017. "Locating multiple types of charging facilities for battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 30-55.
    18. Liu, Zhaocai & Chen, Zhibin & He, Yi & Song, Ziqi, 2021. "Network user equilibrium problems with infrastructure-enabled autonomy," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 207-241.
    19. 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.
    20. Massimo Pappalardo & Giandomenico Mastroeni & Mauro Passacantando, 2016. "Merit functions: a bridge between optimization and equilibria," Annals of Operations Research, Springer, vol. 240(1), pages 271-299, May.

    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:gam:jsusta:v:14:y:2022:i:18:p:11521-:d:914579. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.