Bayesian Dynamic Linear Model with Adaptive Parameter Estimation for Short-Term Travel Speed Prediction
Bayesian dynamic linear model is a promising method for time series data analysis and short-term forecasting. One research issue concerns how the predictive model adapts to changes in the system, especially when shocks impact system behavior. In this study, the authors propose an adaptive dynamic linear model to adaptively update model parameters for online system state prediction. The proposed method is an automatic approach based on the feedback of prediction errors at each time slot without the needs of external intervention. The experimental study on short-term travel speed prediction shows that the proposed method can significantly reduce the prediction errors of the traditional dynamic linear model and outperform two state-of-the-art methods in the case of major system behavior changes.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
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Supplemental Notes:
- © 2019 Tai-Yu Ma and Yoann Pigné. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Authors:
- Ma, Tai-Yu
- Pigne, Yoann
- Publication Date: 2019
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Maps; References; Tables;
- Pagination: 10p
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Serial:
- Journal of Advanced Transportation
- Volume: 2019
- Issue Number: Article ID 5314520
- Publisher: John Wiley & Sons, Incorporated
- ISSN: 0197-6729
- EISSN: 2042-3195
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Bayes' theorem; Data analysis; Dynamic models; Forecasting; Mathematical prediction; Systems analysis; Time series
- Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 01717129
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 18 2019 9:17AM