Abstract
This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF) based state estimation and Expectation-Maximization (EM) based parameter estimation (identification) confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.
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