Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91429
| Title: | A noise-immune boosting framework for short-term traffic flow forecasting | Authors: | Zheng, S Zhang, S Song, Y Lin, Z Jiang, D Zhou, T |
Issue Date: | 2021 | Source: | Complexity, 2021, v. 2021, 5582974 | Abstract: | Accurate short-term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data-driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy-to-implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short-term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance. | Publisher: | Hindawi | Journal: | Complexity | ISSN: | 1076-2787 | EISSN: | 1099-0526 | DOI: | 10.1155/2021/5582974 | Rights: | Copyright © 2021 Shiqiang Zheng et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication Zheng, S., Zhang, S., Song, Y., Lin, Z., Jiang, D., & Zhou, T. (2021). A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting. Complexity, 2021 is available at https://doi.org/10.1155/2021/5582974 |
| Appears in Collections: | Journal/Magazine Article |
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| 5582974.pdf | 2.23 MB | Adobe PDF | View/Open |
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