IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10378-d1530682.html
   My bibliography  Save this article

City-Level Integrated Traffic Management with User Preferences Under Connected Environment

Author

Listed:
  • Hao Yang

    (Civil Engineering Department, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
    These authors contributed equally to this work.)

  • Kentaro Oguchi

    (Toyota InfoTech Labs, 465 N Bernardo Ave, Mountain View, CA 94043, USA
    These authors contributed equally to this work.)

Abstract

In transportation systems, road users have diverse preferences when planning their trips and responding to traffic conditions in a large city. Connected vehicles can capture the preferences of individual users for trip planning, leading to improved road performance. However, managing a large number of connected vehicles with differing user preferences in a large city is a daunting task. This paper develops an integrated traffic management system with the consideration of user preferences to optimize the performance of each user. In the system, connected vehicles are introduced to estimate traffic conditions and costs associated with different user preferences. The system will utilize the information to search for multi-layer vehicle control instructions that account for user preferences in mobility, energy consumption, and driving comfort. Microscopic simulations were carried out to assess the system’s efficacy in mitigating road congestion, reducing fuel consumption, and restricting turns. The results reveal that implementing the system can reduce vehicle delay by up to 32%, fuel consumption by 4%, and left and right turns by 24%. Additionally, the paper evaluates the impact of market shares of connected vehicles with different preferences to analyze their performance at different stages of connected vehicle development. The work can contribute to the development of advanced transportation services in future cities and enhance urban mobility and energy sustainability.

Suggested Citation

  • Hao Yang & Kentaro Oguchi, 2024. "City-Level Integrated Traffic Management with User Preferences Under Connected Environment," Sustainability, MDPI, vol. 16(23), pages 1-15, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10378-:d:1530682
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10378/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/23/10378/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Novoa, Clara & Storer, Robert, 2009. "An approximate dynamic programming approach for the vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 196(2), pages 509-515, July.
    2. Han, Youngjun & Chen, Danjue & Ahn, Soyoung, 2017. "Variable speed limit control at fixed freeway bottlenecks using connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 113-134.
    3. Donati, Alberto V. & Montemanni, Roberto & Casagrande, Norman & Rizzoli, Andrea E. & Gambardella, Luca M., 2008. "Time dependent vehicle routing problem with a multi ant colony system," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1174-1191, March.
    4. Maria Luisa Tumminello & Elżbieta Macioszek & Anna Granà & Tullio Giuffrè, 2023. "A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    5. Nie, Yu (Marco) & Wu, Xing, 2009. "Shortest path problem considering on-time arrival probability," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 597-613, July.
    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. Maksymilian Mądziel, 2024. "Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights," Energies, MDPI, vol. 17(12), pages 1-25, June.
    2. Jiang, Jiwan & Zhou, Yang & Wang, Xin & Ahn, Soyoung, 2024. "On dynamic fundamental diagrams: Implications for automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    3. Deng, Qichen & Santos, Bruno F., 2022. "Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 299(3), pages 814-833.
    4. Ricardo Gatica & Pablo Miranda, 2011. "Special Issue on Latin-American Research: A Time Based Discretization Approach for Ship Routing and Scheduling with Variable Speed," Networks and Spatial Economics, Springer, vol. 11(3), pages 465-485, September.
    5. Lee, Jisun & Joung, Seulgi & Lee, Kyungsik, 2022. "A fully polynomial time approximation scheme for the probability maximizing shortest path problem," European Journal of Operational Research, Elsevier, vol. 300(1), pages 35-45.
    6. Fleming, Christopher L. & Griffis, Stanley E. & Bell, John E., 2013. "The effects of triangle inequality on the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 1-7.
    7. Bertazzi, Luca & Bosco, Adamo & Laganà, Demetrio, 2015. "Managing stochastic demand in an Inventory Routing Problem with transportation procurement," Omega, Elsevier, vol. 56(C), pages 112-121.
    8. Shen, Jin & Zhao, Jiandong & Yu, Zhixin & Zheng, Shiteng & Jiang, Rui, 2025. "The elimination and absorption mechanism of oscillatory motion wave based on jam-absorption driving for mixed traffic flow in intelligent connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
    9. Liu, Yiming & Yu, Yang & Baldacci, Roberto & Tang, Jiafu & Sun, Wei, 2025. "Optimizing carbon emissions in green logistics for time-dependent routing," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    10. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    11. Bi Chen & William Lam & Agachai Sumalee & Qingquan Li & Hu Shao & Zhixiang Fang, 2013. "Finding Reliable Shortest Paths in Road Networks Under Uncertainty," Networks and Spatial Economics, Springer, vol. 13(2), pages 123-148, June.
    12. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    13. Ehmke, Jan Fabian & Campbell, Ann Melissa, 2014. "Customer acceptance mechanisms for home deliveries in metropolitan areas," European Journal of Operational Research, Elsevier, vol. 233(1), pages 193-207.
    14. Lu, Jiawei & Nie, Qinghui & Mahmoudi, Monirehalsadat & Ou, Jishun & Li, Chongnan & Zhou, Xuesong Simon, 2022. "Rich arc routing problem in city logistics: Models and solution algorithms using a fluid queue-based time-dependent travel time representation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 143-182.
    15. Zohreh Hosseini Nodeh & Ali Babapour Azar & Rashed Khanjani Shiraz & Salman Khodayifar & Panos M. Pardalos, 2020. "Joint chance constrained shortest path problem with Copula theory," Journal of Combinatorial Optimization, Springer, vol. 40(1), pages 110-140, July.
    16. Lu, Ruicheng & Ma, Minghui & Wang, Yansong & Lu, Jiaxuan & Liang, Shidong, 2023. "Dynamic areas strategy design for variable speed limiting at fixed freeway bottlenecks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    17. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    18. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    19. Jean-François Cordeau & Gianpaolo Ghiani & Emanuela Guerriero, 2014. "Analysis and Branch-and-Cut Algorithm for the Time-Dependent Travelling Salesman Problem," Transportation Science, INFORMS, vol. 48(1), pages 46-58, February.
    20. Justin C. Goodson & Barrett W. Thomas & Jeffrey W. Ohlmann, 2016. "Restocking-Based Rollout Policies for the Vehicle Routing Problem with Stochastic Demand and Duration Limits," Transportation Science, INFORMS, vol. 50(2), pages 591-607, May.

    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:gam:jsusta:v:16:y:2024:i:23:p:10378-:d:1530682. 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.