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A novel traffic signal control formulation

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  • Lo, Hong K.

Abstract

A novel traffic signal control formulation is developed through a mixed integer programming technique. The formulation considers dynamic traffic, uses dynamic traffic demand as input, and takes advantage of a convergent numerical approximation to the hydrodynamic model of traffic flow. As inherent from the underlying hydrodynamic model, this formulation covers the whole range of the fundamental relationships between speed, flow, and density. Kinematic waves of the stop-and-go traffic associated with traffic signals are also captured. Because of this property, one does not need to tune or switch the model for the different traffic conditions. It "automatically" adjusts to the different traffic conditions. We applied the model to three demand scenarios in a simple network. The results seemed promising. This model produced timing plans that are consistent with models that work for unsaturated conditions. In gridlock conditions, it produced a timing plan that was better than conventional queue management practices.

Suggested Citation

  • Lo, Hong K., 1999. "A novel traffic signal control formulation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(6), pages 433-448, August.
    • Handle: RePEc:eee:transa:v:33:y:1999:i:6:p:433-448
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    1. Han, Ke & Gayah, Vikash V. & Piccoli, Benedetto & Friesz, Terry L. & Yao, Tao, 2014. "On the continuum approximation of the on-and-off signal control on dynamic traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 61(C), pages 73-97.
    2. 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.
    3. Mohebifard, Rasool & Hajbabaie, Ali, 2019. "Optimal network-level traffic signal control: A benders decomposition-based solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 252-274.
    4. 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.
    5. Li, Pengfei & Mirchandani, Pitu & Zhou, Xuesong, 2015. "Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 103-130.
    6. Jiancheng Long & Wai Yuen Szeto, 2019. "Link-Based System Optimum Dynamic Traffic Assignment Problems in General Networks," Operations Research, INFORMS, vol. 67(1), pages 167-182, January.
    7. Fei, Xinyu & Wang, Xingmin & Yu, Xian & Feng, Yiheng & Liu, Henry & Shen, Siqian & Yin, Yafeng, 2023. "Traffic signal control under stochastic traffic demand and vehicle turning via decentralized decomposition approaches," European Journal of Operational Research, Elsevier, vol. 310(2), pages 712-736.
    8. Chow, Andy H.F. & Lo, Hong K., 2007. "Sensitivity analysis of signal control with physical queuing: Delay derivatives and an application," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 462-477, May.
    9. Han, Ke & Gayah, Vikash V., 2015. "Continuum signalized junction model for dynamic traffic networks: Offset, spillback, and multiple signal phases," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 213-239.
    10. Wada, Kentaro & Usui, Kento & Takigawa, Tsubasa & Kuwahara, Masao, 2018. "An optimization modeling of coordinated traffic signal control based on the variational theory and its stochastic extension," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 907-925.
    11. Xu, Guangming & Liu, Yihan & Gao, Yihan & Liu, Wei, 2023. "Integrated optimization of train stopping plan and seat allocation scheme for railway systems under equilibrium travel choice and elastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    12. Jiancheng Long & Ziyou Gao & Xiaomei Zhao & Aiping Lian & Penina Orenstein, 2011. "Urban Traffic Jam Simulation Based on the Cell Transmission Model," Networks and Spatial Economics, Springer, vol. 11(1), pages 43-64, March.
    13. Islam, Tarikul & Vu, Hai L. & Hoang, Nam H. & Cricenti, Antonio, 2018. "A linear bus rapid transit with transit signal priority formulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 163-184.
    14. Chi-kwong Wong & Yiu-yin Lee, 2020. "Lane-Based Traffic Signal Simulation and Optimization for Preventing Overflow," Mathematics, MDPI, vol. 8(8), pages 1-28, August.
    15. Daganzo, Carlos F. & Lehe, Lewis J. & Argote-Cabanero, Juan, 2018. "Adaptive offsets for signalized streets," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 926-934.
    16. Keyvan-Ekbatani, Mehdi & Kouvelas, Anastasios & Papamichail, Ioannis & Papageorgiou, Markos, 2012. "Exploiting the fundamental diagram of urban networks for feedback-based gating," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1393-1403.
    17. Mendes, G.A. & da Silva, L.R. & Herrmann, H.J., 2012. "Traffic gridlock on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 362-370.
    18. Wang, Yi & Szeto, W.Y. & Han, Ke & Friesz, Terry L., 2018. "Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 370-394.
    19. He, Sheng-Xue, 2016. "Will a higher free-flow speed lead us to a less congested freeway?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 17-38.
    20. Wang, David Z.W. & Lo, Hong K., 2010. "Global optimum of the linearized network design problem with equilibrium flows," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 482-492, May.
    21. Zhou, Xuesong, 2017. "Recasting and optimizing intersection automation as a connected-and-automated-vehicle (CAV) scheduling problem: A sequential branch-and-bound search approach in phase-time-traffic hypernetworkAuthor-N," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 479-506.
    22. Hong K. Lo, 2001. "A Cell-Based Traffic Control Formulation: Strategies and Benefits of Dynamic Timing Plans," Transportation Science, INFORMS, vol. 35(2), pages 148-164, May.
    23. Lo, Hong K. & Szeto, W. Y., 2002. "A cell-based variational inequality formulation of the dynamic user optimal assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 421-443, June.

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