Transportation Flow Prediction Based on Graph Attention Echo State Network
Authors: 
Tian Gan, Beining Xu, Jin LiAuthors Info & Claims
Pages 708 - 713
Abstract
Abstract—Traffic flow prediction is of great importance in applications such as traffic management and urban planning. The complex spatial and temporal dependence of traffic flow between different roads poses a great challenge for accurate real-time traffic flow prediction. Traditional traffic flow prediction methods rely on the assumption of data smoothness, and the prediction accuracy decreases significantly in the face of complex, variable and large amount of traffic flow data. Spatio-temporal prediction models based on graph neural networks and recurrent neural networks can achieve better prediction accuracy, but there are still some problems, such as the need for a known static graph structure, inadequate spatial extraction and long training time of the model. To improve traffic flow prediction accuracy and real-time performance, this paper proposes a novel end-to-end deep learning framework called graph attention echo state network (GAESN), which uses attention mechanism and echo state network to extract spatio-temporal features. Experimental results on four real traffic flow datasets show that our proposed model achieves 17.35, 21.34, 24.12 and 17.31 in mean absolute error(MAE); 29.31, 32.67, 37.51and 26.84 in root mean square error(RMSE); 16.76%, 15.44%, 10.33% and 10.94% in mean absolute percentage error(MAPE), respectively. Compared with other existing models, this model reduces the number of parameters to be trained and the time required for model training, and also improves the accuracy of traffic flow prediction.
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Published In
May 2023
1025 pages
ISBN:9798400700705
DOI:10.1145/3603781
Copyright © 2023 ACM.
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Published: 27 July 2023
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CNIOT'23
CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things
May 26 - 28, 2023
Xiamen, China
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Overall Acceptance Rate 39 of 82 submissions, 48%
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