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Paradoxes of reservation-based intersection controls in traffic networks

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  • Levin, Michael W.
  • Boyles, Stephen D.
  • Patel, Rahul

Abstract

Reservation-based intersection control is a revolutionary idea for using connected autonomous vehicle technologies to improve intersection controls. Vehicles individually request permission to follow precise paths through the intersection at specific times from an intersection manager agent. Previous studies have shown that reservations can reduce delays beyond optimized signals in many demand scenarios. The purpose of this paper is to demonstrate that signals can outperform reservations through theoretical and realistic examples. We present two examples that exploit the reservation protocol to prioritize vehicles on local roads over vehicles on arterials, increasing the total vehicle delay. A third theoretical example demonstrates that reservations can encourage selfish route choice leading to arbitrarily large queues. Next, we present two realistic networks taken from metropolitan planning organization data in which reservations perform worse than signals. We conclude with significantly positive results from comparing reservations and signals on the downtown Austin grid network using dynamic traffic assignment. Overall, these results indicate that network-based analyses are needed to detect adverse route choices before traffic signals can be replaced with reservation controls. In asymmetric intersections (e.g. local road-arterial intersections), reservation controls can cause several potential issues. However, in networks with more symmetric intersections such as a downtown grid, reservations have great potential to improve traffic.

Suggested Citation

  • Levin, Michael W. & Boyles, Stephen D. & Patel, Rahul, 2016. "Paradoxes of reservation-based intersection controls in traffic networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 90(C), pages 14-25.
  • Handle: RePEc:eee:transa:v:90:y:2016:i:c:p:14-25
    DOI: 10.1016/j.tra.2016.05.013
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    1. Carlos F. Daganzo, 1998. "Queue Spillovers in Transportation Networks with a Route Choice," Transportation Science, INFORMS, vol. 32(1), pages 3-11, February.
    2. Tampère, Chris M.J. & Corthout, Ruben & Cattrysse, Dirk & Immers, Lambertus H., 2011. "A generic class of first order node models for dynamic macroscopic simulation of traffic flows," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 289-309, January.
    3. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    4. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
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    Citations

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    Cited by:

    1. Michael W. Levin, 2019. "A Combinatorial Dynamic Network Trajectory Reservation Algorithm for Connected Autonomous Vehicles," Networks and Spatial Economics, Springer, vol. 19(1), pages 27-55, March.
    2. Lu, Gongyuan & Shen, Zili & Liu, Xiaobo & Nie, Yu (Marco) & Xiong, Zhiqiang, 2022. "Are autonomous vehicles better off without signals at intersections? A comparative computational study," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 26-46.
    3. Chen, Xiangdong & Lin, Xi & Li, Meng & He, Fang & Meng, Qiang, 2023. "A nearly throughput-maximum knotted intersection design and control for connected and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 44-79.
    4. Biyao Wang & Yi Han & Siyu Wang & Di Tian & Mengjiao Cai & Ming Liu & Lujia Wang, 2022. "A Review of Intelligent Connected Vehicle Cooperative Driving Development," Mathematics, MDPI, vol. 10(19), pages 1-31, October.
    5. Luetian Sun & Rui Song, 2022. "Improving Efficiency in Congested Traffic Networks: Pareto-Improving Reservations through Agent-Based Timetabling," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    6. Rey, David & Levin, Michael W., 2019. "Blue phase: Optimal network traffic control for legacy and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 105-129.
    7. Yu, Chunhui & Sun, Weili & Liu, Henry X. & Yang, Xiaoguang, 2019. "Managing connected and automated vehicles at isolated intersections: From reservation- to optimization-based methods," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 416-435.
    8. Chen, Xiaolong & Hu, Manjiang & Xu, Biao & Bian, Yougang & Qin, Hongmao, 2022. "Improved reservation-based method with controllable gap strategy for vehicle coordination at non-signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    9. Yuanyuan Wu & Feng Zhu, 2021. "Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    10. Myungwhan Choi & Areeya Rubenecia & Hyo Hyun Choi, 2019. "Reservation-based traffic management for autonomous intersection crossing," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    11. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    12. Wang, Hua & Meng, Qiang & Chen, Shukai & Zhang, Xiaoning, 2021. "Competitive and cooperative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 322-346.
    13. Mohamed Alawadhi & Jumah Almazrouie & Mohammed Kamil & Khalil Abdelrazek Khalil, 2020. "A systematic literature review of the factors influencing the adoption of autonomous driving," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1065-1082, December.

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