Abstract
Large road networks overflowing with vehicles have called for increased traffic congestion, the impact of which is felt on an everyday basis and across different dimensions like decreased traveller satisfaction, increased fuel usage and increased air pollution among many other troubles. Improved traffic control strategies that can self-learn to adapt their decisioning in response to dynamic changes in the traffic flows and are capable of mitigating overall network congestion as opposed to localized congestion at intersections, are of great importance in mitigating traffic congestion. Traffic control strategies which were rule-based or historical-demand based were over-simplified and could not scale to large real-world road networks. To effectively control traffic congestion at scale, the need for co-operation and communication between the different intersections of a large road network is crucial. Multi-agent reinforcement learning methods are an apt choice for traffic signal control of large scale road networks as they can learn to perform predictive control actions that will reduce overall network congestion dynamically at scale. In this paper, we extend the work done in [24] to traffic signal timing (green phase duration) control using Multi-agent Twin Delayed Deep Deterministic Policy Gradients (MATD3) on large scale real-world road networks. The solution strategy was exposed to simulations of different road networks and time-varying traffic flows. The experimental results showed that our strategy is robust to the different kinds of road networks and vehicular traffic flows, and consistently outperformed its adaptive and rule-based counterparts by significantly reducing the average vehicular delay and queue length.
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References
Abdulhai, B., Pringle, R., Karakoulas, G.J.: Reinforcement learning for true adaptive traffic signal control. J. Transp. Eng. 129(3) (2003)
Ackermann, J., Gabler, V., Osa, T., Sugiyama, M.: Reducing overestimation bias in multi-agent domains using double centralized critics. arXiv preprint arXiv:1910.01465 (2019)
Balaji, P., Srinivasan, D.: Type-2 Fuzzy Logic based urban traffic management. Eng. Appl. Artif. Intell. 24(1), 12–22 (2011). https://doi.org/10.1016/j.engappai.2010.08.007. https://www.sciencedirect.com/science/article/pii/S0952197610001624
Bellman, R.: A Markovian decision process. J. Math. Mech. 679–684 (1957)
Chu, T., Wang, J., Codecà, L., Li, Z.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086–1095 (2019)
Dunne, M.C., Potts, R.B.: Algorithm for traffic control. Oper. Res. 12(6), 870–881 (1964)
Gazis, D.C.: Optimum control of a system of oversaturated intersections. Oper. Res. 12(6), 815–831 (1964)
Ge, H., Song, Y., Wu, C., Ren, J., Tan, G.: Cooperative deep q-learning with q-value transfer for multi-intersection signal control. IEEE Access 7, 40797–40809 (2019)
Genders, W., Razavi, S.: Using a deep reinforcement learning agent for traffic signal control. arXiv preprint arXiv:1611.01142 (2016)
Head, L., Mirchandani, P., Shelby, S.: The Rhodes prototype: a description and some results (1998)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Prabuchandran, K.J., Hemanth Kumar, A.N., Bhatnagar, S.: Multi-agent reinforcement learning for traffic signal control. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2529–2534 (2014). https://doi.org/10.1109/ITSC.2014.6958095
Prashanth, L.A., Bhatnagar, S.: Threshold tuning using stochastic optimization for graded signal control. IEEE Trans. Veh. Technol. 61(9), 3865–3880 (2012). https://doi.org/10.1109/TVT.2012.2209904
Prashanth, L.A., Bhatnagar, S.: Reinforcement learning with function approximation for traffic signal control. IEEE Trans. Intell. Transp. Syst. 12(2), 412–421 (2011). https://doi.org/10.1109/TITS.2010.2091408
Li, L., Lv, Y., Wang, F.Y.: Traffic signal timing via deep reinforcement learning. IEEE/CAA J. Automatica Sinica 3(3), 247–254 (2016)
Liang, X., Du, X., Wang, G., Han, Z.: Deep reinforcement learning for traffic light control in vehicular networks. arXiv preprint arXiv:1803.11115 (2018)
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)
Lowe, R., Wu, Y.I., Tamar, A., Harb, J., Pieter Abbeel, O., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Lowrie, P.: Scats: Sydney co-ordinated adaptive traffic system: a traffic responsive method of controlling urban traffic (1990)
Mauro, V., Di Taranto, C.: Utopia. IFAC Proc. Volumes 23(2), 245–252 (1990). https://doi.org/10.1016/S1474-6670(17)52678-6. https://www.sciencedirect.com/science/article/pii/S1474667017526786. iFAC/IFIP/IFORS Symposium on Control, Computers, Communications in Transportation, Paris, France, 19–21 September
Prashanth, L.A., Bhatnagar, S.: Reinforcement learning with average cost for adaptive control of traffic lights at intersections. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1640–1645 (2011). https://doi.org/10.1109/ITSC.2011.6082823
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (2014)
Riccardo, R., Massimiliano, G.: An empirical analysis of vehicle time headways on rural two-lane two-way roads. Procedia Soc. Behav. Sci. 54, 865–874 (2012)
Shanmugasundaram, P., Bhatnagar, S.: Robust traffic signal timing control using multiagent twin delayed deep deterministic policy gradients. In: ICAART (2), pp. 477–485 (2022)
Shanmugasundaram, P., Sinha, A.: Intelligent traffic control using double deep q networks for time-varying traffic flows. In: 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 64–69 (2021). https://doi.org/10.1109/SPIN52536.2021.9565961
Smith, M.: Traffic control and route-choice; a simple example. Transp. Res. Part B: Methodol. 13(4), 289–294 (1979)
Tampuu, A., et al.: Multiagent cooperation and competition with deep reinforcement learning. CoRR abs/1511.08779 (2015). http://arxiv.org/abs/1511.08779
Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279–292 (1992)
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Shanmugasundaram, P., Bhatnagar, S. (2022). Co-operative Multi-agent Twin Delayed DDPG for Robust Phase Duration Optimization of Large Road Networks. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science(), vol 13786. Springer, Cham. https://doi.org/10.1007/978-3-031-22953-4_6
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