Kiam Beng Yeo

Agent-Based Traffic Flow Optimization at Multiple Signalized Intersections

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 so that immediate actions can be taken when congestion is happening to minimize the number of vehicles in queue. QLTS has better performance than the existing common fixed-time traffic signalization (FTS) in dealing with the ramp flow due to its flexibility in changing the traffic signal with accordance to the traffic conditions and necessity.

Trajectory Clustering for Behavioral Pattern Learning in Transportation Surveillance

The development of an efficient traffic flow monitoring system has been the main focus for many researchers working in the field. Due to the rapid development in urbanization, the complexity of traffic intersections provides challenges for researchers to detect the underlying traffic scenes. With the emerging video based surveillance system, vehicle trajectory can be extracted for observation and prediction via behavioral pattern learning. Prior to the learning, clustering of the extracted vehicle trajectory data is performed to group the data based on similarity measures. In this paper, the implementation of clustering algorithm on the trajectory data is analyzed and issues concerning the trajectory clustering are discussed.

Optimization of urban traffic network signalization using genetic algorithm

This work aims to minimize average delay for an urban signalized intersection under oversaturated condition using genetic algorithm (GA). Relieving urban traffic congestion is an urgent call for traffic engineering. The effectiveness of traffic signalization is one of the key solutions to reduce congestion, but regrettably the current traffic signal control system is not fully optimized for handling oversaturated condition. Therefore, this work proposes GA to optimize traffic signals for reducing average delay at a signalized crossed intersection under oversaturated condition. A comprehensive traffic model based on Public Works Department, Malaysia has been developed as the platform. The average delay experienced by vehicles to traverse the crossed intersection is used as the performance metric to evaluate performances of the proposed algorithm. Simulation results show GA is able to control the traffic signals for minimizing the average delay to 55 sec/veh or equivalent to level of service (LOS) D.

Modeling of Acoustic Channel for Underwater Wireless Communication System in AUV Application

This paper present the modeling of seawater acoustic channel to yield the optimum carry frequency for autonomous underwater vehicle (AUV) wireless communication system. Many underwater acoustic wireless communications had been explore whereby the exploration is mainly done for a specific distance. However, the wireless communication distance of an AUV with its control vessel varies due to the task at certain depth. This current AUV is design with an operation depth of 0 to 2000m. Therefore, the existing model had limitation when the operation depth varies base on the task assignment. This is because the optimum frequency to noise ration varies with respect to the distance of the transmission. To surmount the existing modeling method, this paper presents an improved method to identify the optimum carry frequency for AUV wireless communication system. Mathematical model with the relation of frequency, distance and noise is formulated and simulation is carried out to tabulate the optimum frequencies at various distances. From the frequency band, the average frequency is identified as the optimum transmission carries frequency. The identified frequency will be used for further hardware development.