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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    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.
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