000			04265nas a2200433 a 4500
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008			160328s2014 th uu|m rtt 0| a1eng d
035			_a.b12141422
099		9	_aAIT Thesis no.TE-13-07
100	0		_aSupapat Mahadthai
245	1	2	_aA new approach of traffic state estimation by implementing ergodic theory into Kalman Filtering Technique
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2014
300			_a76 leaves : _bill.
490	1		_aThesis ; _vno. TE-13-07
500			_aA thesis submitted in partial fulfillment of the requirements for thedegree of Master of Engineering inTransportation Engineering, School of Engineeering and Technology
520			_aTraffic 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.
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2014
650		0	_aTraffic estimation _xSimulation methods
700	0		_aKunnawee Kanitpong, _eChairperson
700	1		_aNakatsuji, Takashi, _eExamination committee
700	1		_aSantoso, Djoen San, _eExamination committee
710	2		_aMinistry of Public Works (MPW), Indonisia, _eScholarship donor
710	2		_aAsian Institute of Technology Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. TE-13-07
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B04137
907			_a.b12141422 _bmnait _cz
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998			_b0 _c160328 _dm _ea _fz _g2
962			_a000:001:PDF:b1214142:002468:0:0:0:0:0:0 _tAbstract--AIT Thesis no.TE-13-07 _vn
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