vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff89770e0000001124020001000400 Congestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flow increase the difficulties towards the ultimate solutions. Thus, traffic congestions still remain as unsolvable problems to the current traffic control system. In this study, artificial intelligence method has been introduced in the traffic light system to alter the traffic signal timing plan to optimize the traffic flows. Q-learning algorithm in this study has enhanced the traffic light system with learning ability. The learning mechanism of Q-learning enables traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each others to a common goal of ensuring the fluency of the traffic flows within the traffic network. The simulated results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimize the traffic flow accordingly.
Yit Kwong Chin1, Heng Jin Tham1, N.S.V. Kameswara Rao1, Nurmin Bolong1 and Kenneth Tze Kin Teo2
Universiti Malaysia Sabah, Malaysia
Reinforcement Learning, Q-Learning, Traffic Networks, Traffic Signal Timing Plan Management, Multi-Agents Systems
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 3 , Issue: 2 , Pages: 485-491 )
Date of Publication :
January 2013
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