International Journal of Economics, Finance and Management Sciences

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Research Article |

Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction

Accurate prediction of traffic speed plays a key role in easing traffic congestion and improving road utilization efficiency. However, traditional traffic analysis methods often fail to capture complex traffic patterns. With the rapid development of artificial intelligence, traffic prediction using machine learning models has become a focal point of research. This study aims to explore the application of machine learning models in traffic speed analysis and prediction, by constructing a multi-model fusion method through stacking-based ensemble. Initially, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, Multilayer Perceptron (MLP), Linear Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) were selected as base models to predict the traffic speed. Then, their predictive performance was improved by optimizing the model parameters through Bayesian optimization algorithm. After contrast experiments, LR was adopted as a meta-regressor to merge the predictive factors of the optimized base models into a stacking-based ensemble model, improving the performance of traffic speed prediction further. Finally, the proposed ensemble model was evaluated using multiple traffic datasets. The experimental validation demonstrates that the ensemble model achieves outstanding performance in predicting traffic speed. The findings of this study highlight the potential of machine learning models, particularly the stacking-based ensemble method, in predicting the traffic speed.

Ensemble Model, Data Mining, Traffic Speed Prediction, Machine Learning, Bayesian Optimization

Yuanzhe Cheng, Haoyang Lv, Hanrui Chen, Chengjie Ni, Yuyang Hu. (2023). Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction. International Journal of Economics, Finance and Management Sciences, 11(5), 255-260.

Copyright © 2023 Authors retain the copyright of this article.
This article 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.

1. Wang, J. Y., Gu, Q., Wu, J. J., Liu, G. N. and Xiong, Z. (2016). Traffic speed prediction and congestion source exploration: A deep learning method. In Proceedings of 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain, December 12-15, pp. 499-508.
2. Ding, Q. Y., Wang, X. F., Zhang, X. Y. and Sun, Z. Q. (2011). Forecasting traffic volume with space-time ARIMA model. Advanced Materials Research, 156, 979-983.
3. Jia, Y. H., Wu, J. P. and Du, Y. M. (2016). Traffic speed prediction using deep learning method. In Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). Rio de Janeiro, Brazil, November 1-4, pp. 1217-1222.
4. Sheng, D. M., Zheng, X. X. and Yi, X. T. (2019). Intelligent traffic control system based on cloud computing and big data mining. IEEE Transactions on Industrial Informatics, 15 (12), 6583-6592.
5. Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J. and Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627-635.
6. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y. J. and Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.
7. Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B. and Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13 (2), 361-378.
8. Russ, G., Kruse, R., Schneider, M. and Wagner, P. (2008). Data mining with neural networks for wheat yield prediction. In Proceedings of the 8th Industrial Conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, Leipzig, Germany, July 16-18, pp. 47-56.
9. Komi, M., Li, J., Zhai, Y. and Zhang, X. G. (2017). Application of data mining methods in diabetes prediction. In Proceedings of 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, June 2-4, pp. 1006-1010.
10. Wu, C. H., Kuo, S. W. and Kao, S. C. (2019), Classification-based data mining applied in vehicle accident prediction. Fuzzy Systems and Data Mining, 320, 218-223.
11. Huang, X. H., Ye, Y. M., Yang, X. F. and Xiong, L. Y. (2023). Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Systems with Applications, 222, 119779.
12. He, H. L., Zhang, W. Y. and Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105-117.
13. Cui, S. Z., Yin, Y. Q., Wang, D. J., Li, Z. W. and Wang, Y. Z. (2021). A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing, 101, 107038.
14. Khoei, T. T., Labuhn, M. C., Caleb, T. D., Hu, W. C. and Kaabouch, N. (2021). A stacking-based ensemble learning model with Genetic Algorithm for detecting early stages of Alzheimer's disease. In Proceedings of 2021 IEEE International Conference on Electro/Information Technology, Mount Pleasant, USA, May 14-15, pp. 215-222.
15. Wang, Y. Y., Wang, D. J., Geng, N., Wang, Y. Z., Yin, Y. Q. and Jin, Y. C. (2019). Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Applied Soft Computing, 77, 188-204.
16. Cai, P. L., Wang, Y. P., Lu, G. Q., Chen, P., Ding, C. and Sun, J. P. (2016). A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies, 62, 21-34.
17. Castro-Neto, M., Jeong, Y. S., Jeong, M. K. and Han, L. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36 (3), 6164-6173.
18. Hamed, M. M., Almasaeid, H. R. and Said, Z. M. B. (1995). Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering-asce, 121 (3), 249-254.
19. Wei, Y., Chen, M. C. (2012). Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies, 21 (1), 148-162.
20. Wang, M., Wu, L. B., Li, M., Wu, D., Shi, X. C. and Ma, C. (2022). Meta-learning based spatial-temporal graph attention network for traffic signal control. Knowledge-based Systems, 250, 109166.
21. Ma, X. L., Tao, Z. M., Wang, Y. H., Yu, H. Y. and Wang, Y. P. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187-197.
22. Du, L., Gao, R., Suganthan, P. N. and Wang, D. Z. W. (2022). Bayesian optimization based dynamic ensemble for time series forecasting. Information Sciences, 591, 155-175.