Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning


pallavi mandhare
Dr. Jyoti Yadav
Prof. Vilas Kharat
Prof. C. Y. Patil


The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches. The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a realtime response at intersection geometrics and controls the traffic signals accordingly. The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm.


How to Cite
pallavi mandhare, Dr. Jyoti Yadav, Prof. Vilas Kharat, & Prof. C. Y. Patil. (2021). Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning. International Journal of Next-Generation Computing, 12(2), 190–199.


  1. Abdoos, M., Mozayani, N., and Bazzan, A. L. 2013. Holonic multi-agent system for traffic signals control. Engineering Applications of Artificial Intelligence 26, 5-6, 1575–1587.
  2. Abdulhai, B., Pringle, R., and Karakoulas, G. J. 2003. Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering 129, 3, 278–285.
  3. Akbar, P. A., Couture, V., Duranton, G., and Storeygard, A. 2018. Mobility and congestion in urban india. Tech. rep., National Bureau of Economic Research.
  4. Arel, I., Liu, C., Urbanik, T., and Kohls, A. G. 2010. Reinforcement learning-based multi- agent system for network traffic signal control. IET Intelligent Transport Systems 4, 2, 128–135.
  5. Brockfeld, E., Barlovic, R., Schadschneider, A., and Schreckenberg, M. 2001. Op- timizing traffic lights in a cellular automaton model for city traffic. Physical review E 64, 5, 056132.
  6. Camponogara, E. and Kraus, W. 2003. Distributed learning agents in urban traffic control.
  7. In Portuguese Conference on Artificial Intelligence. Springer, 324–335.
  8. Chin, Y. K., Bolong, N., Kiring, A., Yang, S. S., and Teo, K. T. K. 2011. Q-learning based traffic optimization in management of signal timing plan. International Journal of Simulation, Systems, Science & Technology 12, 3, 29–35.
  9. Deng, L. 2014. A tutorial survey of architectures, algorithms, and applications for deep learning.
  10. APSIPA Transactions on Signal and Information Processing 3.
  11. Duggan, M., Duggan, J., Howley, E., and Barrett, E. 2016. An autonomous network aware vm migration strategy in cloud data centres. In 2016 International Conference on Cloud and Autonomic Computing (ICCAC). IEEE, 24–32.
  12. El-Tantawy, S. and Abdulhai, B. 2010. An agent-based learning towards decentralized and coordinated traffic signal control. In 13th International IEEE Conference on Intelligent Transportation Systems. IEEE, 665–670.
  13. Gao, J., Shen, Y., Liu, J., Ito, M., and Shiratori, N. 2017. Adaptive traffic signal control: Deep reinforcement learning algorithm with experience replay and target network. arXiv preprint arXiv:1705.02755 .
  14. Genders, W. and Razavi, S. 2016. Using a deep reinforcement learning agent for traffic signal control. arXiv preprint arXiv:1611.01142 .
  15. Greguric´, M., Vujic´, M., Alexopoulos, C., and Miletic´, M. 2020. Application of deep
  16. reinforcement learning in traffic signal control: An overview and impact of open traffic data.
  17. Applied Sciences 10, 11, 4011.
  18. Krajzewicz, D., Erdmann, J., Behrisch, M., and Bieker, L. 2012. Recent development and applications of sumo-simulation of urban mobility. International journal on advances in systems and measurements 5, 3&4.
  19. LeCun, Y., Bengio, Y., and Hinton, G. 2015. Deep learning. nature (2015). May; 521 (7553): 436 10.1038/nature14539 .
  20. Ritcher, S. 2007. Traffic light scheduling using policy-gradient reinforcement learning. In The International Conference on Automated Planning and Scheduling., ICAPS.
  21. Sutton, R. S. 1992. A special issue of machine learning on reinforcement learning. Machine learning 8.
  22. Van der Pol, E. and Oliehoek, F. A. 2016. Coordinated deep reinforcement learners for traffic light control. Proceedings of Learning, Inference and Control of Multi-Agent Systems (at NIPS 2016).
  23. Vidali, A., Crociani, L., Vizzari, G., and Bandini, S. 2019. A deep reinforcement learning approach to adaptive traffic lights management. In WOA. 42–50.
  24. Watkins, C. J. C. H. 1989. Learning from delayed rewards.
  25. Wei, H., Chen, C., Zheng, G., Wu, K., Gayah, V., Xu, K., and Li, Z. 2019. Presslight: Learning max pressure control to coordinate traffic signals in arterial network. In Proceed- ings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1290–1298.
  26. Wei, H., Zheng, G., Yao, H., and Li, Z. 2018. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2496–2505.