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 |
Traffic Flow, Prediction, Bagging, Ensemble Learning Model
| [1] | Kim, E. Y. MRF model based real‐time traffic flow prediction with support vector regression, Electronics Letters. 2017, 53(4) 243-245. |
| [2] | Chen, J. B., Li, D. M., Zhang, G. L., & Zhang, X. L. Localized space-time autoregressive parameters estimation for traffic flow prediction in urban road networks, Applied Sciences.2018, 8(2), 277. |
| [3] | Chen, C., Li, K. L., Teo, S. G., Zou, X. F., Li, K. Q., & Zeng, Z. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks, ACM Transactions on Knowledge Discovery from Data (TKDD). 2020, 14(4), 1-23. |
| [4] | Chen, K., Chen, F., Lai, B. S., Jin, Z. M., Liu, Y., Li, K., et al. Dynamic spatio-temporal graph-based CNNs for traffic flow prediction, IEEE Access. 2020, 8, 185136-185145. |
| [5] | Wang, H., He, X. Y., Chen, L. Y., Yin, J. R., Han, L., Liang, H., et al. Cognition-driven traffic simulation for unstructured road networks, Journal of Computer Science and Technology. 2020, 35, 875-888. |
| [6] | Ma, X. Y., Hu, X. W., Weber, T., & Schramm, D. Evaluation of accuracy of traffic flow generation in SUMO, Applied Sciences. 2021, 11(6), 2584. |
| [7] | Aljuaydi, F., Wiwatanapataphee, B., & Wu, Y. H. Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events, Alexandria engineering journal. 2023, 65, 151-162. |
| [8] | Redhu, P., Redhu, P., & Kumar, K. Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM, Physica A: Statistical Mechanics and its Applications. 2023, 625, 129001. |
| [9] | Hunt, E. B., Marin, J., & Stone, P. J. Experiments in induction, The American Journal of Psychology. 1966, 80(4). |
| [10] | Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews. 2015, 71, 804-818. |
| [11] | Efron, B. Logistic regression, survival analysis, and the Kaplan-Meier curve, Journal of the American Statistical Association. 1988, 83(402), 414-425. |
| [12] | Ferreira, A., and Giraldi, G. Convolutional neuralnetwork approaches to granite tiles classification, Expert Systems with Applications. 2017, 84, 1-11. |
| [13] | Chen, J., Zeng, G. Q., Zhou, W. N., Du, W., and Lu, K. D. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization, Energy Conversion and Management. 2018, 165, 681-695. |
| [14] | Liu, D., Wang, J. L., and Wang, H. Short-term wind speed forecasting based on spectral clustering and optimized echo state networks, Renewable Energy. 2015, 78, 599-608. |
| [15] | Ma, F., Yu, L. M., Ye, L. S., Yao, D. D., & Zhuang, W. F. Length-of-stay prediction for pediatric patients with respiratory diseases using decision tree methods, IEEE Journal of Biomedical and Health Informatics. 2020, 24(9), 2651-2662. |
| [16] | Milanović, S., Marković, N., Pamučar, D., Gigović, L., Kostić, P., & Milanović, S. D. Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method, Forests. 2020, 12(1), 5. |
| [17] | Huang, F. M., Cao, Z. S., Guo, J. F., Jiang, S. H., Li, S., & Guo, Z. Z. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping, Catena. 2020, 191, 104580. |
| [18] | Hu, X. J., Liu, T., Hao, X. T., & Lin, C. X. Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction, The Journal of Supercomputing. 2022, 78(10), 12686-12709. |
| [19] | Alharkan, H., Habib, S., & Islam, M. Solar power prediction using dual stream CNN-LSTM architecture, Sensors. 2023, 23(2), 945. |
| [20] | Saboor, A., Hussain, A., Agbley, B. L. Y., ul Haq, A., Li, J. P., & Kumar, R. Stock market index prediction using machine learning and deep learning techniques, Intelligent Automation & Soft Computing. 2023, 37(2), 1325-1344. |
| [21] | Chen, C., Petty, K., Skabardonis, A., Varaiya, P., & Jia, Z. F. Freeway performance measurement system: mining loop detector data, Transportation Research Record. 2001, 1748(1), 96-102. |
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
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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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}
}
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 -
International Division, The Affiliated High School to Hangzhou Normal University, Hangzhou, China
School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
