Implicit Scenario Mixture Models for Travel Time Estimation
Dependent structure between the travel times between different links are modeled via Gaussian copula mixture models (GCMM) in this paper. GCMM are the generalization of Gaussian Mixture models (GMM) using the concept of copula. Its mathematical definition is given and the properties of likelihood function are studied in this paper. Based on these properties, extended Expectation Maximum algorithms are developed for estimating parameters for the mixture of copulas while marginal distributions corresponding to each component is estimated using separate nonparametric statistical methods. In the experiment, GCMM can achieve better goodness-of-fitting given the same number of clusters as GMM; furthermore, GCMM can utilize unsynchronized data on each dimension to achieve deeper mining of data.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Wan, Ke
- Kornhauser, Alain
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Conference:
- Transportation Research Board 96th Annual Meeting
- Location: Washington DC, United States
- Date: 2017-1-8 to 2017-1-12
- Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 96th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Algorithms; Data mining; Estimating; Statistical analysis; Travel time
- Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 01630092
- Record Type: Publication
- Report/Paper Numbers: 17-00894
- Files: TRIS, TRB, ATRI
- Created Date: Mar 27 2017 9:30AM