Browsing by Author "Yit Kwong Chin"

Now showing 1 - 7 of 7
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    Agent-Based Optimization for Multiple Signalized Intersections using Q-Learning
    (Faculty of Engineering, Universiti Malaysia Sabah Kota Kinabalu, Malaysia, 2014-12-30) Yit Kwong Chin; Kiam Beng Yeo; Kenneth Tze Kin Teo; Helen Sin Ee Chuo; Min Keng Tan
    Relieving urban traffic congestion has always been an urgent call in a dynamic traffic network. The objective of this research is to control the traffic flow within a traffic network consists of multiple signalized intersections with traffic ramp. The massive traffic network problem is dealt through Q-learning actuated traffic signalization (QLTS), where the traffic phases will be monitored as immediate actions can be taken during congestion to minimize the number of vehicles in queue. QLTS is tested under two cases and has better performance than common fixed-time traffic signalization (FTS). When dealing with the ramp flow, QLTS has flexibility to change the traffic signals according to the traffic conditions and necessity.
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    Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator
    (Modeling, Simulation & Computing Laboratory, Minerals & Materials Research Unit School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Malaysia, 2012) Wei Yeang Kow; Wei Leong Khong; Yit Kwong Chin; Ismail Saad; Kenneth Tze Kin Teo
    Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle’s position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.
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    Enhancement of Particle Filter Resampling in Vehicle Tracking via Genetic Algorithm
    (UKSim-AMSS 6th European Modelling Symposium, 2012) Wei Leong Khong; Wei Yeang Kow; Yit Kwong Chin; Mei Yeen Choong; Kenneth Tze Kin Teo
    Vehicle tracking is an essential approach that can help to improve the traffic surveillance or assist the road traffic control. Recently, the development of video surveillance infrastructure has incited the researchers to focus on the vehicle tracking by using video sensors. However, the amount of the on-road vehicle has been increased dramatically and hence the congestion of the traffic has made the occlusion scene become a challenge task for video sensor based tracking. Conventional particle filter will encounter tracking error during and after occlusion. Besides that, it also required more iteration to continuously track the vehicle after occlusion. Thus, particle filter with genetic operator resampling has been proposed as the tracking algorithm to faster converge and keep track on the target vehicle under various occlusion incidents. The experimental results show that enhancement of the particle filter with genetic algorithm manage to reduce the particle sample size.
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    Minimizing Network Coding Nodes in Multicast Tree Construction via Genetic Algorithm
    (2012) Shee Eng Tan; Zhan Wei Siew; Yit Kwong Chin; Scott Carr Ken Lye; Kenneth Tze Kin Teo
    Network coding is a method that increases network throughput by encoding several packets with single packet size length and forwards the packet in a single transmission time slot. At the same time, network coding increases the complexity of packets management and delay of network due to the waiting time for network coding opportunity. A solution based on improved genetic algorithm is proposed to optimize the network coding node resources in network coding. Genetic algorithm will search a multicast tree that fulfils the desired throughput with a desired multicast rate. Mutation rate of the genetic algorithm will change based on the previous solution to avoid from being stuck on the local optima. The simulation result shows that with given multicast rate, improved genetic algorithm is able to search and construct multicast tree with minimal usage of network coding nodes.
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    Performance Analysis of Intelligent Transport Systems (ITS) with Adaptive Transmission Scheme
    (2012) Scott Carr Ken Lye; Shee Eng Tan; Yit Kwong Chin; Bih Lii Chua; Kenneth Tze Kin Teo
    Various means of modern transports have already taken the initiative to incorporate computing and communication technology. This cross field area is coined as intelligent transportation systems (ITS). Transport systems of the future require fast and precise monitoring to ensure safety of such systems is guaranteed. Thus, the communication aspect demands seamless and minimal error whilst delivering vital data. However, the dynamic surroundings always post a challenge to wireless communications, taking account into a myriad of interference such as multipath fading, shadowing, dispersing and path loss. Furthermore, there is also case of mobility. In this paper, the performance of wireless communications with adaptive modulation and coding in vehicular scenarios were compared against rigid transmission techniques. Simulations were conducted over different mobility channel models against Signal-to-Noise ratio (SNR) to provide thorough analysis. Performance measure of Bit Error Rate (BER) and Packet Error Rate (PER) were used to provide understanding of the overall picture.
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    Q-Learning Based Traffic Optimization in Management of Signal Timing Plan
    (2011) Yit Kwong Chin; Nurmin Bolong; Aroland Kiring; Soo Siang Yang; Kenneth Tze Kin Teo
    Occurrences 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.
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    Q-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network
    (2012) Yit Kwong Chin; Wei Yeang Kow; Wei Leong Khong; Min Keng Tan; Kenneth Tze Kin Teo
    Traffic flow optimization within traffic networks has been approached through different kinds of methods. One of the methods is to reconfigure the traffic signal timing plan. However, dynamic characteristic of the traffic flow is not able to be resolved by the conventional traffic signal timing plan management. As a result, traffic congestion still remains as an unsolved problem. Thus, in this study, artificial intelligence algorithm has been introduced in the traffic signal timing plan to enable the traffic management systems’ learning ability. QLearning algorithm acts as the learning mechanism for traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each other to a common goal of ensuring the fluency of the traffic flows within traffic network. The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.