Open AccessShort‐term traffic flow prediction of road network based on deep learning
Author(s) -
Han Lei,
Huang YiShao
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0133
Subject(s) - traffic flow (computer networking) , computer science , term (time) , extreme learning machine , traffic generation model , artificial intelligence , deep belief network , intelligent transportation system , deep learning , flow network , data mining , machine learning , real time computing , engineering , artificial neural network , mathematics , mathematical optimization , physics , quantum mechanics , civil engineering , computer security
Due to the fact that existing traffic flow forecasting methods cannot completely reflect the real time traffic situation of the road network, a new method of short‐term traffic flow prediction is proposed based on deep learning in this study. Firstly, in order to improve the efficiency of data processing, a method of road network data compression is proposed based on correlation analysis and CX decomposition. Secondly, the traffic flow data are divided into trend term and random fluctuation term by spectral decomposition method, and the influence of trend term on prediction accuracy is removed. Finally, by combining a deep belief network model and a kernel extreme learning machine classifier as the prediction model, the essential characteristics of the traffic flow data are extracted by using DBN at the bottom of the network, and the extracted results are input into the kernel extreme learning machine to predict the traffic flow. The actual regional road network traffic flow data are tested to verify the effectiveness of the proposed short‐time network traffic flow forecasting method. The results show that the proposed method can not only save 90% of the running time but also the average prediction accuracy of each road section can reach 92%.