SOC Press, Indonesia Symposium on Computing (IndoSC) 2016

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Optimasi Waiting Time pada Simulasi Intelligent Traffic Light Control menggunakan Markov Decision Process
Beryl Ramadhian Aribowo


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Abstract


This research proposes Traffic Light Control as the main topic. It is a topic which received many attention from the researchers due to it is the most determining factor to traffic optimization. Some basic and advanced algorithms have been devised by researchers to overcome this problem since years ago. Specifically, this research would also does the same, by producing a Traffic Light Control scheme by using a model based Reinforcement Learning which is Markov Decision Process (MDP). This particular MDP model is obtained through observation to the environment which is an infrastructure that comes from a traffic light simulator, Green Light District. This research produces an MDP model that is able to optimize the waiting time of the tested infrastructure. The approach which is used to build the MDP model is by observing the density of each lane that is going inside into a single junction to form the states, while the action is taken from the identifier of a lane which has green as its traffic light’s sign within a single junction. Based on the test results, this particular MDP model outperformed the other basic Traffic Light Control algorithms with Average Junction Waiting Time (AJWT) of 29.886 seconds.

Reference


[1] Rahmawati, N. (2016). Conversational Recommender System with Explanation Facility Using Semantic Reasoning. International Journal on Information and Communication Technology (IJoICT), 2(1), 1-12. Crossref

[2] Effendy, Veronikha., Novantirani, Anita., Sabariah, M.K. 2016. “Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method”.

[3] Semarak, J. (1996). Intelligent traffic lights control by fuzzy logic. Malaysian Journal of Computer Science, 9(2), 29-35.

[4] Singh, L., Tripathi, S., & Arora, H. (2009). Time optimization for traffic signal control using genetic algorithm. International Journal of Recent Trends in Engineering, 2(2), 4-6.

[5] Hirankitti, V., & Krohkaew, J. (2007, March). An agent approach for intelligent traffic-light control. In Modelling & Simulation, 2007. AMS'07. First Asia International Conference on (pp. 496-501). IEEE. Crossref

[6] Wiering, Marco., Veenen, Jelle., Vreeken, Jilles., Koopman, Arne. 2004. “Intelligent Traffic Light Control”.

[7] Abdoos, M., Mozayani, N., & Bazzan, A. L. (2011, October). Traffic light control in non-stationary environments based on multi agent q-learning. In 2011 14th International IEEE conference on intelligent transportation systems (ITSC) (pp. 1580-1585). IEEE. Crossref

[8] Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction (Vol. 1, No. 1). Cambridge: MIT press.

[9] Steingrover, M., Schouten, R., Peelen, S., Nijhuis, E., & Bakker, B. (2005, October). Reinforcement Learning of Traffic Light Controllers Adapting to Traffic Congestion. In BNAIC (pp. 216-223).

Last modified: 2016-11-02