IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v160y2022ics1366554522000679.html
   My bibliography  Save this article

Multi-rhythm control for heterogeneous traffic and road networks in CAV environments

Author

Listed:
  • Chen, Xiangdong
  • Lin, Xi
  • Li, Meng
  • He, Fang

Abstract

Heterogeneity is widely observed in various aspects of urban transport, such as road types, vehicle dynamics, and demand patterns, which greatly complexifies transport systems and hinders the successful control and management of traffic. On one hand, intelligent strategies are required to relieve the loss of efficiency caused by the mixed traffic flow composed of vehicles with different speeds; on the other hand, the spatial heterogeneity of travel demand distribution requires pertinent control schemes to improve the network-level mobility and the phenomenon of oversaturation under heavy traffic also demands an appropriate way to resolve. Leveraging the advantages of connected and automated vehicle (CAV) technology, this study proposes a multi-rhythm control (MRC) scheme to accommodate heterogeneous network traffic in a CAV environment with 100% penetration rate. While the rhythmic control (RC) proposed by (Lin et al., 2021) could organize traffic efficiently and in an orderly manner, the multi-rhythm strategy has greater potential to not only improve the efficiency of heterogeneous traffic by the design of different virtual platoons (VPs) catering to different vehicle types, but also address the spatial heterogeneity of network traffic by implementing differentiated cycle lengths among the road network. For the optimal design of the MRC scheme, a two-step model is developed to minimize the total travel cost, where the first step focuses on the mesoscopic level to jointly optimize the multi-rhythm scheme, VP design and lane configuration for both undersaturated and oversaturated cases, while the second step focuses on the microscopic design to optimize the space–time trajectories of VPs under the premise of collision free. Numerical examples and simulation experiments are conducted to test the performances of the MRC scheme compared to a naive version of RC with a homogeneous rhythm and a traffic signal control (TSC) strategy. The results show that among all control strategies, the MRC scheme has the best performance under all kinds of demand patterns, which is reflected in both the lowest travel cost and the highest network throughput.

Suggested Citation

  • Chen, Xiangdong & Lin, Xi & Li, Meng & He, Fang, 2022. "Multi-rhythm control for heterogeneous traffic and road networks in CAV environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:transe:v:160:y:2022:i:c:s1366554522000679
    DOI: 10.1016/j.tre.2022.102672
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554522000679
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2022.102672?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. Chang, Tang-Hsien & Sun, Guey-Yin, 2004. "Modeling and optimization of an oversaturated signalized network," Transportation Research Part B: Methodological, Elsevier, vol. 38(8), pages 687-707, September.
    2. Denos C. Gazis, 1964. "Optimum Control of a System of Oversaturated Intersections," Operations Research, INFORMS, vol. 12(6), pages 815-831, December.
    3. Lo, Hong K. & Chang, Elbert & Chan, Yiu Cho, 2001. "Dynamic network traffic control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(8), pages 721-744, September.
    4. Chang, Tang-Hsien & Lin, Jen-Ting, 2000. "Optimal signal timing for an oversaturated intersection," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 471-491, August.
    5. Wang, Peirong (Slade) & Li, Pengfei (Taylor) & Chowdhury, Farzana R. & Zhang, Li & Zhou, Xuesong, 2020. "A mixed integer programming formulation and scalable solution algorithms for traffic control coordination across multiple intersections based on vehicle space-time trajectories," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 266-304.
    6. De, Manoranjan & Giri, B.C., 2020. "Modelling a closed-loop supply chain with a heterogeneous fleet under carbon emission reduction policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    7. Xiao, Yiyong & Konak, Abdullah, 2016. "The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 146-166.
    8. Lu, Gongyuan & Nie, Yu(Marco) & Liu, Xiaobo & Li, Denghui, 2019. "Trajectory-based traffic management inside an autonomous vehicle zone," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 76-98.
    9. Wu, Wei & Zhang, Fangni & Liu, Wei & Lodewijks, Gabriel, 2020. "Modelling the traffic in a mixed network with autonomous-driving expressways and non-autonomous local streets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    10. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
    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. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    3. D’Acierno, Luca & Gallo, Mariano & Montella, Bruno, 2012. "An Ant Colony Optimisation algorithm for solving the asymmetric traffic assignment problem," European Journal of Operational Research, Elsevier, vol. 217(2), pages 459-469.
    4. Ngoduy, Dong & Nguyen, Cuong H.P. & Lee, Seunghyeon & Zheng, Zuduo & Lo, Hong K., 2024. "A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    5. Chang, Tang-Hsien & Sun, Guey-Yin, 2004. "Modeling and optimization of an oversaturated signalized network," Transportation Research Part B: Methodological, Elsevier, vol. 38(8), pages 687-707, September.
    6. Akeb, Hakim & Moncef, Btissam & Durand, Bruno, 2018. "Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 223-233.
    7. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    8. Stephany Isabel Vallarta-Serrano & Ana Bricia Galindo-Muro & Riccardo Cespi & Rogelio Bustamante-Bello, 2023. "Analysis of GHG Emission from Cargo Vehicles in Megacities: The Case of the Metropolitan Zone of the Valley of Mexico," Energies, MDPI, vol. 16(13), pages 1-19, June.
    9. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    10. Yuan, Quan & Hua, Zhongsheng & Shen, Bin, 2021. "An automated system of emissions permit trading for transportation firms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    11. Zhang, Abraham & Wang, Jason X. & Farooque, Muhammad & Wang, Yulan & Choi, Tsan-Ming, 2021. "Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    12. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    13. Yang, Yuxiang & Goodarzi, Shadi & Jabbarzadeh, Armin & Fahimnia, Behnam, 2022. "In-house production and outsourcing under different emissions reduction regulations: An equilibrium decision model for global supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    14. Nanyondo, Josephine & Kasumba, Henry, 2024. "Analysis of heterogeneous vehicular traffic: Using proportional densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    15. Junwoo Song & Simon Hu & Ke Han & Chaozhe Jiang, 2020. "Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation," Networks and Spatial Economics, Springer, vol. 20(3), pages 675-702, September.
    16. Mohan, Ranju & Ramadurai, Gitakrishnan, 2021. "Multi-class traffic flow model based on three dimensional flow–concentration surface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    17. Ennio Cascetta & Mariano Gallo & Bruno Montella, 2006. "Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment models," Annals of Operations Research, Springer, vol. 144(1), pages 301-328, April.
    18. Liu, Wenjing & Delahaye, Daniel & Cetek, Fulya Aybek & Zhao, Qiuhong & Notry, Philippe, 2024. "Comparison of performance between PMS and trombone arrival route topologies in terminal maneuvering area," Journal of Air Transport Management, Elsevier, vol. 115(C).
    19. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    20. Senlai Zhu & Hantao Yu & Congjun Fan, 2024. "Travel Plan Sharing and Regulation for Managing Traffic Bottleneck Based on Blockchain Technology," Sustainability, MDPI, vol. 16(4), pages 1-20, February.

    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:transe:v:160:y:2022:i:c:s1366554522000679. 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/600244/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.