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Improving Efficiency in Congested Traffic Networks: Pareto-Improving Reservations through Agent-Based Timetabling

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  • Luetian Sun

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Rui Song

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

Abstract

In an urban transportation network, congestion occurs in the form of a queue behind a bottleneck. Many studies have considered a reservation-based optimization approach for queuing systems. To control the traffic density behind a bottleneck so that it does not exceed the link capacity, and to reduce the emissions and improve the sustainability of cities, we propose a new mobility service system to offer a Pareto-improving schedule for both the portion of agents making reservations and others with fixed departure time schedules. This reservation system takes the agents’ (i.e., users or vehicles here) actual arrival and departure times from a conventional system without reservations as the preferred time windows at both the origins and destinations. Such a centralized mobility service system could maintain or improve the end-to-end traveling performance for all users. The proposed reservation and end-to-end timetabling problem is formulated as a multicommodity flow optimization problem in a discretized space–time network. We use a modified dynamic programming method for the reservation strategy on the space–time network and further adopt the alternative direction method of multiplier (ADMM) based on prime and dual theory to solve the large-scale instances. A comprehensive discussion is also provided regarding the technical challenges and potential solutions when operating such a system in a real-world setting.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2211-:d:750215
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    as
    1. Fosgerau, Mogens & Small, Kenneth A., 2013. "Hypercongestion in downtown metropolis," Journal of Urban Economics, Elsevier, vol. 76(C), pages 122-134.
    2. Liu, Jiangtao & Zhou, Xuesong, 2016. "Capacitated transit service network design with boundedly rational agents," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 225-250.
    3. Lamotte, Raphaël & de Palma, André & Geroliminis, Nikolas, 2017. "On the use of reservation-based autonomous vehicles for demand management," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 205-227.
    4. 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.
    5. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    6. Ukkusuri, Satish V. & Han, Lanshan & Doan, Kien, 2012. "Dynamic user equilibrium with a path based cell transmission model for general traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1657-1684.
    7. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    8. Wang, Xiaolei & Yang, Hai, 2012. "Bisection-based trial-and-error implementation of marginal cost pricing and tradable credit scheme," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1085-1096.
    9. Huang, He & Gao, Song, 2012. "Optimal paths in dynamic networks with dependent random link travel times," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 579-598.
    10. Wang, Xiaotian & Wang, Xin, 2019. "Flexible parking reservation system and pricing: A continuum approximation approach," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 408-434.
    11. Guo, Xiaolei & Yang, Hai, 2010. "Pareto-improving congestion pricing and revenue refunding with multiple user classes," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 972-982, September.
    12. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    13. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    14. Carlos F. Daganzo & Reinaldo C. Garcia, 2000. "A Pareto Improving Strategy for the Time-Dependent Morning Commute Problem," Transportation Science, INFORMS, vol. 34(3), pages 303-311, August.
    15. Chen, Xinyuan & Zhang, Wei & Guo, Xiaomeng & Liu, Zhiyuan & Wang, Shuaian, 2021. "An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    16. Ouyang, Yanfeng & Yang, Haolin & Daganzo, Carlos F., 2021. "Performance of reservation-based carpooling services under detour and waiting time restrictions," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 370-385.
    17. Boyacı, Burak & Zografos, Konstantinos G. & Geroliminis, Nikolas, 2017. "An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 214-237.
    18. Eiji Kometani & Tsuna Sasaki, 1959. "A Safety Index for Traffic with Linear Spacing," Operations Research, INFORMS, vol. 7(6), pages 704-720, December.
    19. 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.
    20. Repoux, Martin & Kaspi, Mor & Boyacı, Burak & Geroliminis, Nikolas, 2019. "Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 82-104.
    21. Zhiling Han & Yanyan Chen & Hui Li & Kuanshuang Zhang & Jiyang Sun, 2019. "Customized Bus Network Design Based on Individual Reservation Demands," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    22. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    23. (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.
    24. Mahmoudi, Monirehalsadat & Zhou, Xuesong, 2016. "Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 19-42.
    25. Wei, Yuguang & Avcı, Cafer & Liu, Jiangtao & Belezamo, Baloka & Aydın, Nizamettin & Li, Pengfei(Taylor) & Zhou, Xuesong, 2017. "Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 102-129.
    26. Chen, Linxi & Yang, Hai, 2012. "Managing congestion and emissions in road networks with tolls and rebates," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 933-948.
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