標題: | Time Dependent Origin-destination Estimation from Traffic Count without Prior Information |
作者: | Cho, Hsun-Jung Jou, Yow-Jen Lan, Chien-Lun 運輸與物流管理系 註:原交通所+運管所 資訊管理與財務金融系 註:原資管所+財金所 Department of Transportation and Logistics Management Department of Information Management and Finance |
關鍵字: | Time-dependent origin-destination estimation;State space model;Gibbs sampler;Kalman Filter;Parallel computing |
公開日期: | 1-六月-2009 |
摘要: | Existing research works on time-dependent origin-destination (O-D) estimation focus on the surveillance data usually assume the prior information of the O-D matrix (or transition matrix) is known (or at least partially known). In this paper, we relax such assumption by combining Gibbs sampler and Kalman filter in a state space model. A solution algorithm with parallel chain convergence control is proposed and implemented. To enhance its efficiency, a parallel structure is suggested with efficiency and speedup demonstrated using PC-cluster. Two numerical examples (one for Taipei Mass Rapid Transit network and the other for Taiwan Area National Freeway network) are included to show the proposed model could be effective of time-dependent origin-destination estimation. |
URI: | http://dx.doi.org/10.1007/s11067-008-9082-7 http://hdl.handle.net/11536/7195 |
ISSN: | 1566-113X |
DOI: | 10.1007/s11067-008-9082-7 |
期刊: | NETWORKS & SPATIAL ECONOMICS |
Volume: | 9 |
Issue: | 2 |
起始頁: | 145 |
結束頁: | 170 |
顯示於類別: | 期刊論文 |