Abstract
In this paper, methods have been explored to effectively optimize traffic signal control to minimize waiting times and queue lengths, thereby increasing traffic flow. The traffic intersection was first defined as a Markov Decision Process, and a state representation, actions and rewards were chosen. Simulation of Urban MObility (SUMO) was used to simulate an intersection and then compare a Round Robin Scheduler, a Feedback Control mechanism and two Reinforcement Learning techniques—Deep Q-Network (DQN) and Advantage Actor-Critic (A2C), as the policy for the traffic signal in the simulation under different scenarios. Finally, the methods were tested on a simulation of a real-world intersection in Bengaluru, India.
Chaudhuri, Masti and Veerendranath: These authors contributed equally to this work.
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References
Wey, W.M.: Model formulation and solution algorithm of traffic signal control in an urban network. Comput. Environ. Urban Syst. 24(4), 355–378 (2000)
Eriskin, E., Karahancer, S., Terzi, S., Saltan, M.: Optimization of traffic signal timing at oversaturated intersections using elimination pairing system. In: Procedia Engineering, vol. 187, pp. 295–300 (2017)
Abdoos, M., Mozayani, N., Bazzan, A.: Traffic light control in non-stationary environments based on multi agent Q-learning. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (2011)
Brys, T., Pham, T., Taylor, M.: Distributed learning and multi-objectivity in traffic light control. Connect. Sci. 26(1), 65–83 (2014)
Gregurić, M., Vujić, M., Alexopoulos, C., Miletić, M.: Application of deep reinforcement learning in traffic signal control: an overview and impact of open traffic data. Appl. Sci. 10(11), 4011 (2020)
Rasheed, F., Yau, K.L.A., Noor, R.M., Wu, C., Low, Y.C.: Deep reinforcement learning for traffic signal control: a review. IEEE Access (2020)
Vidali, A., Crociani, L., Vizzari, G., Bandini, S.: A deep reinforcement learning approach to adaptive traffic lights management. In: WOA, pp. 42–50 (2019)
Mnih, V., Badia, A., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, PMLR (2016)
Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility)-an open-source traffic simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling, MESM20002, pp. 183–187 (2002)
Alegre, L.: SUMO-RL. GitHub, github.com/LucasAlegre/sumo-rl (2019)
Wei, H., Zheng, G., Yao, H., Li, Z.: IntelliLight: A reinforcement learning approach for intelligent traffic light control. In: KDD (2018)
Muthupalaniappan, A., Nair, B.S., Rajan, R.A., Krishnan, R.K.: Dynamic control of traffic signals using traffic data from google maps and road cameras. Int. J. Recent Technol. Eng. (2019)
Zheng, G., Xiong, Y., Zang, X., Feng, J., Wei, H., Zhang, H., Li, Y., Xu, K., Li, Z.: Learning phase completion for traffic signal control. In: CKIM, International Conference on Information and Knowledge Management (2019)
Hilmani, A., Maizate, A., Hassouni, L.: Automated real-time intelligent traffic signal control system for smart cities using wireless sensor networks. In: Wireless Communications and Mobile Computing (2020)
Pandit, K., Ghosal, D., Zhang, H. M., Chuah, C.: Adaptive traffic signal control with vehicular ad hoc networks. IEEE Trans. Veh. Technol. 62(4), 1459–1471 (2013)
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Chaudhuri, H., Masti, V., Veerendranath, V., Natarajan, S. (2022). A Comparative Study of Algorithms for Intelligent Traffic Signal Control. In: Chen, J.IZ., Wang, H., Du, KL., Suma, V. (eds) Machine Learning and Autonomous Systems. Smart Innovation, Systems and Technologies, vol 269. Springer, Singapore. https://doi.org/10.1007/978-981-16-7996-4_19
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