Approaches to Automated Traffic Optimal Control System

Elena Sofronova

Abstract


The paper addresses the problems of optimal control and synthesis of traffic flow control in urban areas. It is proposed to solve these problems within the framework of an automated control system used to create an intelligent transport system (ITS) for large cities. Data on the quantitative characteristics of the flow is obtained from road infrastructure detectors. The road network is described by a directed graph, where nodes correspond to road sections and edges correspond to manoeuvres at intersections. The graph has a variable structure depending on the control. The control is determined by the duration of the traffic light phases at signalised intersections. A universal recurrent model of traffic flow, based on the theory of controlled networks, is used to describe the control of traffic flow. Optimal control problem statements are given for different phase switching modes: within a fixed cycle, without a fixed cycle, within a multicycle. The solution of the multicriteria optimal control problem is given. The use of evolutionary algorithms is proposed to solve the optimal control problem. Then, a control synthesis statement for the traffic flow is proposed. The traffic flow is controlled by selecting the duration of the working phases of the traffic lights with respect to the state of the object. The task is to find a traffic light control function that depends on the state of the traffic flow. To solve the control synthesis problem, it is proposed to use modern numerical methods of symbolic regression. Numerical solution of multicriterial optimal control problem for intersection with real field data is given.

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References


Eom M. Kim BI. The traffic signal control problem for intersections: a review // Eur. Transp. Res. Rev. — 2020. — Vol. 12, no. 50.

Wei H. Zheng G. Gayah V. Li Z. A survey on traffic signal control methods // arXiv:1904.08117 [cs.LG]. — 2020.

Global practices on road traffic signal control: Fixed-time control at isolated intersections / Keshuang Tang, Manfred Boltze, Hideki Nakamura, Zong Tian. — Elsevier, 2019.

Hunt P.B. Robertson D.I. Bretherton R.D. Royale M.C. The SCOOT on-line traffic signal optimization technique // Traffic Engineering Control. — 1982. — Vol. 23. — P. 190–192.

Robertson D.I., Bretherton R.D. Optimizing networks of traffic signals

in real-time SCOOT method // IEEE Trans. Veh. Technol. — 1991. — Vol. 40. — P. 11–15.

Sims A. The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits // IEEE Trans. Veh. Technol. — 1980. — Vol. 29. — P. 130–137.

Cools S.B., Gershenson C., D’Hooghe B. Self-organizing traffic lights: A realistic simulation // Advances in Applied Self-Organizing Systems. — 2013. — P. 45–55.

Varaiya P. Max pressure control of a network of signalized intersections // Transp. Res. Part C Emerg. Technol. — 2013. — Vol. 36. P. 177–195.

Reinforcement learning in urban network traffic signal control: A systematic literature review / M. Noaeen, A. Naik, L. Goodman et al. // Expert Syst. Appl. — 2022. — Vol. 199. — P. 116830.

Intellilight: A reinforcement learning approach for intelligent traffic light control / H. Wei, G. Zheng, H. Yao, Z. Li // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 19–23 August 2018. — 2018. — P. 2496–2505.

Haydari A., Yilmaz Y. Deep reinforcement learning for intelligent transportation systems: A survey // IEEE Trans. Intell. Transp. Syst. — 2022. — Vol. 23. — P. 11–32.

Shabestary S.M.A., Abdulhai B. Deep learning vs. discrete reinforcement learning for adaptive traffic signal control // Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018. — 2018. — P. 286–293.

Zeng J., Hu J., Zhang Y. Adaptive traffic signal control with deep recurrent q-learning // Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 26–30 June 2018. — 2018. — P. 1215–1220.

Chen P., Zhu Z., Lu G. An adaptive control method for arterial signal coordination based on deep reinforcement learning // Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27–30 October 2019. — 2019. — P. 3553–3558.

Expression might be enough: Representing pressure and demand for reinforcement learning based traffic signal control / L. Zhang, Q. Wu, S. Jun et al. // Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA, 17–23 July 2022. — 2022. — P. 26645– 26654.

Van Hasselt H., Guez A., Silver D. Deep reinforcement learning with double q-learning // Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), Austin, TX, USA, 25–30 January 2015. — 2015. — P. 2094–2100.

Dueling network architectures for deep reinforcement learning / Z. Wang, T. Schaul, M. Hessel et al. // Proceedings of the 33rd International Conference on Machine Learning (ICML’16), New York, NY, USA, 19–24 June 2016. — 2016. — P. 1995–2003.

Prioritized experience replay / T. Schaul, J. Quan, I. Antonoglou, D. Silver // Proceedings of the 4th International Conference on Learning Representations (ICLR’16), San Juan, PR, USA, 2–4 May 2016. — 2016.

The Mathematical Theory of Optimal Processes / L.S. Pontryagin, V.G. Boltyanskii, R.V. Gamkrelidze, E.F. Mishechenko. — VIII + 360 S. : New York/London, 1962.

Genealogy of traffic flow models / F. Van Wageningen-Kessels, H. van Lint, K. Vuik, S. Hoogendorn // EURO Journal on Transportation and Logistics. — 2015. — Vol. 4. — P. 445–473.

Introduction to mathematical modeling of traffic flows: textbook / A.V. Gasnikov, S.L. Klenov, E.A. Nurminskij et al. — 362 p. [in Russian] : MIPT, Moscow, 2010.

Diveev A.I. Controlled networks and their applications // Computational Mathematics and Mathematical Physics. — 2008. — Vol. 48, no. 8. — P. 1428–1442.

Sofronova E.A. Hybrid recurrent traffic flow model (URTFM-RNN) // Intelligent Systems and Applications, Proceedings of the 2021 Intelligent Systems Conference (IntelliSys). — 2021. — Vol. 2.

Sofronova E.A., Diveev A.I. Traffic flows optimal control problem with full information // 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece. — 2020. — P. 1–6.

Sofronova E., Diveev A. Controlled networks to solve traffic flows problem // 2022 International Conference on Modern Network Technologies (MoNeTec). — 2022.

Sofronova E.A., Belyakov A.A., Khamadiyarov D.B. Optimal control for traffic flows in the urban road networks and its solution by variational genetic algorithm // Procedia Computer Science. — 2019. — 01. — Vol. 150. — P. 302–308.

Sofronova E.A., Diveev A.I. Universal approach to solution of optimization problems by symbolic regression // Appl. Sci. — 2021. — Vol. 11. — P. 5081.

A fast and elitist multi-objective genetic algorithm: NSGA-II / K. Deb, A. Pratap, S. Agarwal, T. Meyarivan // IEEE Transactions on Evolutionary Computation. — 2002. — Vol. 6, no. 2. — P. 182–197.

Bellman R. Dynamic Programming. — 340 p. : Princeton University Press, Princeton, New Jersey, Sixth Printing, 1972.

Boltyanskiy V.G. Mathematical methods of optimal control, 2nd Edition. — 408 p. [in Russian] : Nauka, Moscow, 1969.

Diveev A.I. Numerical methods for control synthesis problem: monograph. — 192 p. [in Russian] : RUDN University, Moscow, 2019.

Sofronova E.A., A.I. Diveev. Package for simulation and search for the optimal control program for groups of traffic lights using variational genetic algorithm. certificate of state registration of the computer program no. 2020619911 dated August 25, 2020. — 2020.


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