Path Flow Estimation Using Time Varying Coefficient State Space Model

Yow-Jen Jou*, Chien Lun Lan

*Corresponding author for this work

研究成果: Chapter同行評審

摘要

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.
原文English
主出版物標題Computational Methods In Science And Engineering, Vol 2: Advances In Computational Science
編輯G Maroulis, TE Simos
頁面501-+
1148
出版狀態Published - 2009
事件6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008 - Hersonissos, Crete, Greece
持續時間: 25 九月 200830 九月 2008

出版系列

名字AIP Conference Proceedings
1148
ISSN(列印)0094-243X

Conference

Conference6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008
國家Greece
城市Hersonissos, Crete
期間25/09/0830/09/08

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