04265nas a2200433 a 4500 20260818165912.0 160328s2014 th uu|m rtt 0| a1eng d .b12141422 AIT Thesis no.TE-13-07 Supapat Mahadthai A new approach of traffic state estimation by implementing ergodic theory into Kalman Filtering Technique Pathum Thani, Thailand : Asian Institute of Technology, 2014 76 leaves : ill. Thesis ; no. TE-13-07 A thesis submitted in partial fulfillment of the requirements for thedegree of Master of Engineering inTransportation Engineering, School of Engineeering and Technology 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. Thesis (M. Eng.) - Asian Institute of Technology, 2014 Traffic estimation Simulation methods Kunnawee Kanitpong, Chairperson Nakatsuji, Takashi, Examination committee Santoso, Djoen San, Examination committee Ministry of Public Works (MPW), Indonisia, Scholarship donor Asian Institute of Technology Fellowship, Scholarship donor Asian Institute of Technology. Thesis ; no. TE-13-07 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B04137 .b12141422 mnait z 240404 0 160328 m a z 2 000:001:PDF:b1214142:002468:0:0:0:0:0:0 Abstract--AIT Thesis no.TE-13-07 n mnait mnait mnarc 20 40 Barcode : 30050120816995 CREATED : 2016-03-28 RECORD # : i12910181 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120817027 CREATED : 2016-03-28 RECORD # : i12910193 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 1 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050160014402 CREATED : 2016-03-30 RECORD # : i12913741 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 80588 80588 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT Thesis no.TE-13-07 30050120816995 2026-08-18 16:59:12 1 2026-08-18 20 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT Thesis no.TE-13-07 30050120817027 2026-08-18 16:59:12 2 2026-08-18 20 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.TE-13-07 30050160014402 2026-08-18 16:59:12 1 2026-08-18 40