Traffic light control has been a significant test in most major roads in Nigeria. The control of traffic has been so poor in certain spots in Nigeria to such an extent that more timing is being distributed to zones with lesser vehicles while little timing is being allotted to zones of more vehicles. This paper presents an Agent-based system to determine the control of traffic light signals using Reinforcement Learning algorithm by applying Deep Q Learning Techniques. The Reinforcement learning algorithm was trained using a Deep Q-learning technique with a total of 4 input layers, a batch size of 100, learning rate of 0.001 and a training epoch of 800 and a gamma of 0.97. The learning environment was made up with a maximum number of steps of 5400, total numbers of car generated to be 1000, green light duration in 10, yellow light duration to be 4. The number of actions taken by the agent equals 4 on 80 different states. The system helps in reducing traffic congestion by adapting to the learning environment, therefore knowing lanes with more vehicles during and without rush hours. By this, system optimizes the green time effectively by allocating more time to lane with more vehicles during and with rush hours, therefore, reducing the average cumulative delays and average cumulative queued length of vehicles. The result showed that system is efficient in traffic signal control with an average queued vehicle length of 5 to 20 vehicle

Reinforcement learning, Deep Learning, Traffic, Agent, Environment, Stimulation

[1]. P.G. Balaji, X. German, D. Srinivasan “Urban traffic signal control using reinforcement learning agents”, IET Intelligent Transport Systems, Vol.4 issue.3 pp.177-188, 2010.
[2].W. Yizhe, X. Yang, Y. Liu, H. Liang “Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning”, Journal of Advanced Transportation, Vol.2018 issue.1494 pp. 1-9, 2018.
[3]. G. Wade and R. Saiedeh “Evaluating reinforcement learning state representations for adaptive traffic signal control”, Procedia Computer Science Vol. 130, pp. 26-33, 2018.
[4].G. Wade and R. Saiedeh “Asynchronous n-step Q-learning adaptive traffic signal control”, Journal of Intelligent Transportation Systems, Vol.23 issue.4, pp.319-331, 2019.
[5]. J. Kim, J. Sangchul, K. Kwangsik, L. Seungjae “The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment” Intellian Association of Chemical Engineering Vol. 72, pp.91-96, 2019.
[6]. G. Wade and R. Saiedeh “Policy Analysis of Adaptive Traffic Signal Control Using Reinforcement Learning” Journal of Computing in Civil Engineering Vol.34 issue 1 pp.1-5, 2020.
[7]. M. Wiering, J. Vreeken, J. Veenen, A. Koopman “Simulation and Optimization of Traf?c in a City” Conference paper, IEEE, Intelligent Vehicle Symposium 2004.
[8]. K. Daniel, B. Elmar, M. Jurgen, R. Julia, C. Rossel, T. Wolfram, W. Peter, W. Richard “Simulation of modern Traffic Lights Control Systems using the open source Traffic Simulation” Proceedings of the 3rd Industrial Simulation Conference 2005.
[9]. W. Wen “A dynamic and automatic traffic light control expert system for solving the road congestion problem” Expert System Applications, Vol.34 issue.4, pp. 2370-2381 2008.
[10]. M. Killat, H. Hartenstein, R. Luz, S. Hausberger, T. Benz “The impact of traffic-light-to-vehicle communication on fuel consumption and emissions”, Conference of Internet of Things (IOT). 2010.