Open AccessUrban traffic flow forecast based on dual path network
Author(s) -
Li Hao,
Xudong Li,
Yan Kang,
Yachuan Zhang,
Rongjing Bu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1453/1/012162
Subject(s) - computer science , path (computing) , traffic flow (computer networking) , dual (grammatical number) , traffic generation model , data mining , convolutional neural network , convolution (computer science) , real time computing , simulation , artificial neural network , artificial intelligence , computer network , art , literature
Traffic flow prediction is very important for city construction.Time and space factors have a great impact on traffic flow, Traditional traffic prediction methods can only capture temporal correlations and not capture spatial and regional correlations. The use of convolutional neural networks can well capture the correlation between regions and the dependence between time and space, which can make traffic prediction more accurate. Therefore we introduced the dual-path network, and divided the traffic profile after convolution into two paths and trained at the same time. One path is ResNet and one path is DenseNet. The dual path network combines the advantages of these two networks for traffic prediction. The experimental results show that compared with the traditional traffic prediction model, the model not only improves the efficiency of the network but also improves the prediction accuracy of the network.