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A hybrid machine learning approach for congestion prediction and warning
Global traffic management encounters a significant challenge in traffic congestion. This paper presents a hybrid machine learning method for predicting traffic congestion. It leverages Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) network for parameter prediction and combines clustering and classification models, using the K-means algorithm to categorize traffic states into four levels and constructing a KNN classification model based on this segmentation. This results in the K-means-KNN model. Predicted parameters are inputted into the K-means-KNN model for congestion level prediction. Validation with real traffic flow data shows that the CNN-GRU network can capture spatiotemporal features more effectively. For instance, in traffic flow prediction, it reduces the MAPE by 7.39% and 51.14% compared to CNN and GRU, respectively. K-means-KNN excels in traffic state discrimination, achieving a congestion prediction accuracy of 91.8%. These results underscore the efficacy of the hybrid machine learning method in assessing and predicting urban traffic congestion.
A hybrid machine learning approach for congestion prediction and warning
Global traffic management encounters a significant challenge in traffic congestion. This paper presents a hybrid machine learning method for predicting traffic congestion. It leverages Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) network for parameter prediction and combines clustering and classification models, using the K-means algorithm to categorize traffic states into four levels and constructing a KNN classification model based on this segmentation. This results in the K-means-KNN model. Predicted parameters are inputted into the K-means-KNN model for congestion level prediction. Validation with real traffic flow data shows that the CNN-GRU network can capture spatiotemporal features more effectively. For instance, in traffic flow prediction, it reduces the MAPE by 7.39% and 51.14% compared to CNN and GRU, respectively. K-means-KNN excels in traffic state discrimination, achieving a congestion prediction accuracy of 91.8%. These results underscore the efficacy of the hybrid machine learning method in assessing and predicting urban traffic congestion.
A hybrid machine learning approach for congestion prediction and warning
Li, Dongxue (author) / Hu, Yao (author) / Wu, Chuliang (author) / Chen, Wangyong (author) / Wang, Feiyun (author)
Transportation Planning and Technology ; 48 ; 387-411
2025-02-17
25 pages
Article (Journal)
Electronic Resource
English
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