A new approach of traffic state estimation by implementing ergodic theory into Kalman Filtering Technique - Supapat Mahadthai; Kunnawee Kanitpong; Nakatsuji, Takashi; Santoso, Djoen San; Ministry of Public Works (MPW), Indonisia; Asian Institute of Technology Fellowship (Pathum Thani, Thailand : Asian Institute of Technology, 2014)
Traffic information is crucial for both road user side and traffic controller side. Therefore, accuracy and reliabilityof traffic data hasan influentto the decision of both sides.In the past, traffic data were obtained by conventional way like manual count. Currently, there are several modern approachesto get that information such as CCTV cameras and traffic flow sensors, or even use simulation models topredict the traffic state.Since we do not know the true value, the most important thingis how much the information we have close to the real traffic state. Kalman filtering technique (KFT) is a remarkable estimation approach which has been widely used dealing with stochastic problems. Primary concept of KFT is using observed data to adjust the data from transition model, and those data will become an initial state for the next time stepover the time series. The performance of KFT has been proven by many studies that it can adjustthe systems close to the true value.In this study, we focus on application of KFT into traffic state estimation but using traffic simulation software rather than traffic flow models. However, such application has constrain that we cannot use update state as an initial state for the next time step; therefore, we implementing ergodic theory concept to resolve the limitation of the method in this study. The estimation is done with different condition in order to see the influence of difference conditions. The outcome of the estimation method present in this study is quite acceptable if compare with the case that using simulator alone. The speed is the appropriate observation data for the traffic state estimation in this study. However, sometimes the predicted values are not good since they are far from the real value. This result show that the estimation algorithm still cannot use in practical work, and needed to be modified in order to yielding better results.By using the method presented in this study, the overall process of estimation is less complicated but still givesacceptable result.
A thesis submitted in partial fulfillment of the requirements for thedegree of Master of Engineering inTransportation Engineering, School of Engineeering and Technology
Thesis (M. Eng.) - Asian Institute of Technology, 2014
Keywords: Traffic estimation -- Simulation methods