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Dec 14, 2021
Short-Term Traffic Flow Prediction of Highway Based on Machine Learning
Publication: CICTP 2021
ABSTRACT
The rapid development of artificial intelligence provides a new way for the research of transportation systems. Aiming at the problems of short-term traffic flow prediction such as lagging, insufficient time variable characteristics extraction, and low prediction accuracy, this paper uses the correlation of highway traffic flow in time as the basis to extract 4 types of variables closely related to time, and establish 6 Long-Short-Term Memory (LSTM) models respectively. The results show that a combination model that simultaneously considers multiple time variables can effectively reduce the lag in time series prediction. In addition, we establish two comparison models. The results show that the selected variables have both temporal characteristics and non-temporal characteristics. Capturing these characteristics can help improve the accuracy of the model. Finally, the Random Forest (RF) algorithm is used to rank the importance of variables, which further shows that the combined model has a certain feasibility.
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Published online: Dec 14, 2021
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1Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji Univ., Shanghai. Email: [email protected]
2Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji Univ., Shanghai. Email: [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.
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.
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.