Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

Xinyue Cai; Qinyu Jin; Wenyu Zhang

doi:doi:10.11648/j.sjams.20241205.11

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| Peer-Reviewed

Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

Xinyue Cai

, Qinyu Jin^*

, Wenyu Zhang

Published in Science Journal of Applied Mathematics and Statistics (Volume 12, Issue 5)

Received: 29 July 2024 Accepted: 2 September 2024 Published: 10 October 2024

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Abstract

For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 12, Issue 5)
DOI	10.11648/j.sjams.20241205.11
Page(s)	72-79
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 Flow, Prediction, Bagging, Ensemble Learning Model

References

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APA Style

Cai, X., Jin, Q., Zhang, W. (2024). Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Science Journal of Applied Mathematics and Statistics, 12(5), 72-79. https://doi.org/10.11648/j.sjams.20241205.11

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ACS Style

Cai, X.; Jin, Q.; Zhang, W. Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Sci. J. Appl. Math. Stat. 2024, 12(5), 72-79. doi: 10.11648/j.sjams.20241205.11

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AMA Style

Cai X, Jin Q, Zhang W. Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Sci J Appl Math Stat. 2024;12(5):72-79. doi: 10.11648/j.sjams.20241205.11

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@article{10.11648/j.sjams.20241205.11,
  author = {Xinyue Cai and Qinyu Jin and Wenyu Zhang},
  title = {Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model
},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {12},
  number = {5},
  pages = {72-79},
  doi = {10.11648/j.sjams.20241205.11},
  url = {https://doi.org/10.11648/j.sjams.20241205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20241205.11},
  abstract = {For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.
},
 year = {2024}
}

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TY  - JOUR
T1  - Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

AU  - Xinyue Cai
AU  - Qinyu Jin
AU  - Wenyu Zhang
Y1  - 2024/10/10
PY  - 2024
N1  - https://doi.org/10.11648/j.sjams.20241205.11
DO  - 10.11648/j.sjams.20241205.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 72
EP  - 79
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20241205.11
AB  - For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

VL  - 12
IS  - 5
ER  -

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Author Information

Xinyue Cai

International Division, The Affiliated High School to Hangzhou Normal University, Hangzhou, China

Contact Email

http://orcid.org/0009-0001-4707-4619
Qinyu Jin

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0009-0002-4890-5004
Wenyu Zhang

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0000-0002-8906-5411

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Plain Text BibTeX RIS

APA Style

Cai, X., Jin, Q., Zhang, W. (2024). Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Science Journal of Applied Mathematics and Statistics, 12(5), 72-79. https://doi.org/10.11648/j.sjams.20241205.11

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ACS Style

Cai, X.; Jin, Q.; Zhang, W. Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Sci. J. Appl. Math. Stat. 2024, 12(5), 72-79. doi: 10.11648/j.sjams.20241205.11

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AMA Style

Cai X, Jin Q, Zhang W. Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model. Sci J Appl Math Stat. 2024;12(5):72-79. doi: 10.11648/j.sjams.20241205.11

Copy | Download

@article{10.11648/j.sjams.20241205.11,
  author = {Xinyue Cai and Qinyu Jin and Wenyu Zhang},
  title = {Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model
},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {12},
  number = {5},
  pages = {72-79},
  doi = {10.11648/j.sjams.20241205.11},
  url = {https://doi.org/10.11648/j.sjams.20241205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20241205.11},
  abstract = {For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

AU  - Xinyue Cai
AU  - Qinyu Jin
AU  - Wenyu Zhang
Y1  - 2024/10/10
PY  - 2024
N1  - https://doi.org/10.11648/j.sjams.20241205.11
DO  - 10.11648/j.sjams.20241205.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 72
EP  - 79
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20241205.11
AB  - For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

VL  - 12
IS  - 5
ER  -

Copy | Download