A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction
Zhishuai Li; Gang Xiong; Yuanyuan Chen; Yisheng Lv; Bin Hu; Fenghua Zhu; Fei-Yue Wang
2019-11-28
Conference NameIEEE Intelligent Transportation Systems Conference
Conference Date2019-10-27
Conference PlaceAuckland, New Zealand
Abstract

Traffic flow prediction is an important functional component of Intelligent Transportation Systems (ITS). In this paper, we propose a hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA), to efficiently capture the spatial-temporal features in traffic flow. Firstly, we apply graph convolutional network (GCN) to mine the spatial relationships of traffic flow over multiple observation stations, in which the adjacent matrix is determined by a data-driven approach. Then, we feed the output of the GCN model to the long short-term memory (LSTM) model which extracts temporal features embedded in traffic flow. Further, we implement a soft attention mechanism on the extracted spatial-temporal traffic features to make final prediction. We test the proposed method over the PeMS data sets. Experimental results show that the proposed model performs better than the competing methods.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40599
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorYisheng Lv
AffiliationInstitute of Automation, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhishuai Li,Gang Xiong,Yuanyuan Chen,et al. A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction[C],2019.
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