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.
Published in | International Journal of Economics, Finance and Management Sciences (Volume 11, Issue 5) |
DOI | 10.11648/j.ijefm.20231105.15 |
Page(s) | 255-260 |
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), 2023. Published by Science Publishing Group |
Ensemble Model, Data Mining, Traffic Speed Prediction, Machine Learning, Bayesian Optimization
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APA Style
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. https://doi.org/10.11648/j.ijefm.20231105.15
ACS Style
Yuanzhe Cheng; Haoyang Lv; Hanrui Chen; Chengjie Ni; Yuyang Hu. Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction. Int. J. Econ. Finance Manag. Sci. 2023, 11(5), 255-260. doi: 10.11648/j.ijefm.20231105.15
AMA Style
Yuanzhe Cheng, Haoyang Lv, Hanrui Chen, Chengjie Ni, Yuyang Hu. Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction. Int J Econ Finance Manag Sci. 2023;11(5):255-260. doi: 10.11648/j.ijefm.20231105.15
@article{10.11648/j.ijefm.20231105.15, author = {Yuanzhe Cheng and Haoyang Lv and Hanrui Chen and Chengjie Ni and Yuyang Hu}, title = {Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction}, journal = {International Journal of Economics, Finance and Management Sciences}, volume = {11}, number = {5}, pages = {255-260}, doi = {10.11648/j.ijefm.20231105.15}, url = {https://doi.org/10.11648/j.ijefm.20231105.15}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20231105.15}, abstract = {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. }, year = {2023} }
TY - JOUR T1 - Performance Evaluation and Comparison of a Stacking - Based Ensemble Model for Traffic Speed Prediction AU - Yuanzhe Cheng AU - Haoyang Lv AU - Hanrui Chen AU - Chengjie Ni AU - Yuyang Hu Y1 - 2023/10/31 PY - 2023 N1 - https://doi.org/10.11648/j.ijefm.20231105.15 DO - 10.11648/j.ijefm.20231105.15 T2 - International Journal of Economics, Finance and Management Sciences JF - International Journal of Economics, Finance and Management Sciences JO - International Journal of Economics, Finance and Management Sciences SP - 255 EP - 260 PB - Science Publishing Group SN - 2326-9561 UR - https://doi.org/10.11648/j.ijefm.20231105.15 AB - 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. VL - 11 IS - 5 ER -