Browsing by Author "Nurmin Bolong"
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- ItemExploring Q-Learning Optimization in Traffic Signal Timing Plan Management(2011) Yit, Kwong Chin; Lai, Kuan Lee; Nurmin Bolong; Soo, Siang Yang; Tze, Kin Teo (Kenneth)Traffic congestions often occur within the entire traffic network of the urban areas due to the increasing of traffic demands by the outnumbered vehicles on road. The problem may be solved by a good traffic signal timing plan, but unfortunately most of the timing plans available currently are not fully optimized based on the on spot traffic conditions. The incapability of the traffic intersections to learn from their past experiences has cost them the lack of ability to adapt into the dynamic changes of the traffic flow. The proposed Qlearning approach can manage the traffic signal timing plan more effectively via optimization of the traffic flows. Qlearning gains rewards from its past experiences including its future actions to learn from its experience and determine the best possible actions. The proposed learning algorithm shows a good valuable performance that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.
- ItemOPTIMIZATION OF URBAN MULTI-INTERSECTION TRAFFIC FLOW VIA Q-LEARNING(2013) Yit, Kwong Chin; Heng, Jin Tham; N.S.V. Kameswara Rao; Nurmin Bolong; Tze Kin Teo (Kenneth)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.
- ItemQ-Learning Based Traffic Optimization in Management of Signal Timing Plan(2011) Yit Kwong Chin; Nurmin Bolong; Aroland Kiring; Soo Siang Yang; Kenneth Tze Kin TeoOccurrences of traffic congestions within the urban traffic network are increasing in a rapid rate due to the rising traffic demands of the outnumbered vehicles on road. The effectiveness of management from traffic signal timing planner is the key solution to solve the traffic congestions, but unfortunately the current traffic light signal system is not fully optimized based on the dynamic traffic conditions on the road. Adaptable traffic signal timing plan system with ability to learn from their past experiences is needed to overcome the dynamic changes of the urban traffic network. The ability of Q-learning to prospect gains from future actions and obtain rewards from its past experiences allows Q-learning to improve its decisions for the best possible actions. A good valuable performance has been shown by the proposed learning algorithm that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.