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 | |
会议日期 | 2019-10-27 |
会议地点 | Auckland, New Zealand |
英文摘要 | 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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40599] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Yisheng Lv |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 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]. 见:. Auckland, New Zealand. 2019-10-27. |
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