2023, Volume 11
2022, Volume 10
2021, Volume 9
2020, Volume 8
2019, Volume 7
2018, Volume 6
2017, Volume 5
2016, Volume 4
2015, Volume 3
2014, Volume 2
2013, Volume 1
1School of Yingyang Financial Technology, Zhejiang University of Finance and Economics, Hangzhou, China
2Jingying High School, Shijiazhuang, China
3Office of Scientific Research and Foreign Cooperation, Zhejiang-California International NanoSystems Institute, Hangzhou, China
4School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
5School of Mechatronic Engineering, Sanming University, Sanming, China
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. https://doi.org/10.11648/j.ijefm.20231105.15
Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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