Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost

Xin Wang; Fang Fang

doi:doi:10.11648/j.ijtet.20241001.12

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Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost

Xin Wang^*

, Fang Fang

Published in International Journal of Transportation Engineering and Technology (Volume 10, Issue 1)

Received: 31 May 2024 Accepted: 18 June 2024 Published: 23 July 2024

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Abstract

Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.

Published in	International Journal of Transportation Engineering and Technology (Volume 10, Issue 1)
DOI	10.11648/j.ijtet.20241001.12
Page(s)	15-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Traffic Time Prediction, Wavelet Analysis, XGBoost, Bayesian Algorithm

References

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Cite This Article

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APA Style

Wang, X., Fang, F. (2024). Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. International Journal of Transportation Engineering and Technology, 10(1), 15-24. https://doi.org/10.11648/j.ijtet.20241001.12

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ACS Style

Wang, X.; Fang, F. Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. Int. J. Transp. Eng. Technol. 2024, 10(1), 15-24. doi: 10.11648/j.ijtet.20241001.12

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AMA Style

Wang X, Fang F. Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. Int J Transp Eng Technol. 2024;10(1):15-24. doi: 10.11648/j.ijtet.20241001.12

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@article{10.11648/j.ijtet.20241001.12,
  author = {Xin Wang and Fang Fang},
  title = {Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost
},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {10},
  number = {1},
  pages = {15-24},
  doi = {10.11648/j.ijtet.20241001.12},
  url = {https://doi.org/10.11648/j.ijtet.20241001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20241001.12},
  abstract = {Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.
},
 year = {2024}
}

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TY  - JOUR
T1  - Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost

AU  - Xin Wang
AU  - Fang Fang
Y1  - 2024/07/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ijtet.20241001.12
DO  - 10.11648/j.ijtet.20241001.12
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 15
EP  - 24
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20241001.12
AB  - Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.

VL  - 10
IS  - 1
ER  -

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Author Information

Xin Wang

School of Applied Science, Beijing Information Science and Technology University, Beijing, China

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http://orcid.org/0000-0002-2296-3815
Fang Fang

School of Applied Science, Beijing Information Science and Technology University, Beijing, China

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Plain Text BibTeX RIS

APA Style

Wang, X., Fang, F. (2024). Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. International Journal of Transportation Engineering and Technology, 10(1), 15-24. https://doi.org/10.11648/j.ijtet.20241001.12

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ACS Style

Wang, X.; Fang, F. Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. Int. J. Transp. Eng. Technol. 2024, 10(1), 15-24. doi: 10.11648/j.ijtet.20241001.12

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AMA Style

Wang X, Fang F. Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost. Int J Transp Eng Technol. 2024;10(1):15-24. doi: 10.11648/j.ijtet.20241001.12

Copy | Download

@article{10.11648/j.ijtet.20241001.12,
  author = {Xin Wang and Fang Fang},
  title = {Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost
},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {10},
  number = {1},
  pages = {15-24},
  doi = {10.11648/j.ijtet.20241001.12},
  url = {https://doi.org/10.11648/j.ijtet.20241001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20241001.12},
  abstract = {Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost

AU  - Xin Wang
AU  - Fang Fang
Y1  - 2024/07/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ijtet.20241001.12
DO  - 10.11648/j.ijtet.20241001.12
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 15
EP  - 24
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20241001.12
AB  - Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.

VL  - 10
IS  - 1
ER  -

Copy | Download