Structural State Space Model For Real-Time High Way Traffic State Prediction
| Autor: | Yi-Fei Liao, 廖翊斐 |
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| Rok vydání: | 2011 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 99 This study aims to develop an effective approach for predicting real-time short-term traffic states on the freeway. The traffic state of particular interest is the density of a traffic stream, defined as number of vehicles in a unit length of road segment. We assume the actual traffic density as the combination of the regular density pattern (i.e., historical trend), structural deviation from the regular pattern (i.e., the variation in travel time), and random fluctuation. Since the regular density pattern is represented as the median of historical densities, predicting traffic density is equivalent to predicting the structural deviation of traffic density from the regular pattern. The proposed structural space model consists of state and measurement equations. In the state equation, an m-order polynomial trend model is adopted to describe the structural deviation of density. Then, an adaptive Kalman Filter algorithm was developed to solve recursively the two equations and to obtain predicted real-time densities. The traffic density data collected by the loop detectors on freeway number 5 from February to May, 2010, were used to determine the regular pattern. The proposed approach was then used to predict traffic densities on freeway number 5 in June, 2010. The results show that the approach is effective in predicting real-time freeway traffic states and superior to a commonly-used method in the literature. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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