期刊
ELECTRONICS
卷 9, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/electronics9091474
关键词
traffic forecasting; deep learning; diffusion convolution; graph attention; intelligent transportation system
资金
- Guangdong Special Cultivation Funds for College Students' Scientific and Technological Innovation [pdjh2020b0222]
- NSFC [61902232]
- Natural Science Foundation of Guangdong Province [2018A030313291]
- Education Science Planning Project of Guangdong Province [2018GXJK048]
- STU Scientific Research Foundation for Talents [NTF18006]
- 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant [2020LKSFG05D, 2020LKSFG04D]
This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memory (LSTM) block is adopted to capture high-dimensional temporal features. By integrating the two blocks, the ST-TrafficNet can learn the spatial-temporal dependencies of intricate traffic data accurately. The performance of the ST-TrafficNet has been evaluated on two real-world benchmark datasets by comparing it with three commonly-used methods and seven state-of-the-art ones. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the proposed method outperform not only the commonly-used methods, but also the state-of-the-art ones in 15 min, 30 min, and 60 min time-steps.
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