Chapter
Jul 2, 2019
Short Term Traffic Flow Prediction Based on Deep Learning
Publication: CICTP 2019
Abstract
In this paper, three traffic prediction models based on deep learning are used to predict the traffic flow of capital airport. First, we reconstruct the washed traffic flow data to make the prediction results spatial-temporal. After smoothing and standardization, the characteristics of airport traffic data are studied using the stacked automatic coding machine (SAE) model, the long and short memory network (LSTM) model and the control gate recursion (GRU) model, and the final results are predicted by using the regression layer on the top layer. Finally, the results are obtained by anti-standardization, and the three models are obtained. We then compared the reliability of the three models and proved different loss functions.
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Published In
CICTP 2019
Pages: 2457 - 2469
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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Affiliations
School of Traffic Management, People’s Public Security Univ. of China, P.O. Box 102600, Beijing, China. E-mail: [email protected]
School of Traffic Management, People’s Public Security Univ. of China, P.O. Box 102600, Beijing, China. E-mail: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.