A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed
Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, the authors propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.
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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 Eui-Jin Kim et al. 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:
- Kim, Eui-Jin
- Park, Ho-Chul
- Kho, Seung-Young
- Kim, Dong-Kyu
- Publication Date: 2019
Language
- English
Media Info
- Media Type: Print
- Features: Figures; Maps; References; Tables;
- Pagination: 12p
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Serial:
- Journal of Advanced Transportation
- Volume: 2019
- Issue Number: Article ID 3958127
- 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: Correlation analysis; Mathematical prediction; Traffic speed; Travel demand management; Urban highways
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01729590
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 29 2020 2:50PM