Open AccessTraffic prediction based on GCN-LSTM model
Author(s) -
Zhizhu Wu,
Mingxia Huang,
Aihua Zhao,
Zhixun lan
Publication year - 2021
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 - 10.1088/1742-6596/1972/1/012107
Subject(s) - computer science , residual , convolutional neural network , traffic flow (computer networking) , graph , residual neural network , traffic generation model , artificial intelligence , deep learning , data mining , artificial neural network , traffic classification , convolution (computer science) , real time computing , algorithm , computer network , theoretical computer science , quality of service
Traffic flow prediction is an important part of intelligent traffic management system. Because there are many irregular data structures in road traffic, in order to improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model based on deep learning graph convolution neural network (GCN), long-term memory network (LSTM) and residual network (RESNET). GCN is used to extract the features of topology structure in traffic data, LSTM is used to extract the features of time structure, combined with ResNet to optimize the overall model, reduce the occurrence of gradient disappearance or explosion in network degradation, and finally achieve the prediction of traffic flow. According g to the experimental results, the combined traffic flow prediction model used in this paper is closer to the actual traffic flow occurrence than the traditional convolutional neural network model (CNN), and the accuracy is improved.