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-Jun-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
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