Journal
SENSORS
Volume 19, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/s19183836
Keywords
intelligent transportation system; traffic speed prediction; attention mechanism; temporal clustering analysis
Funding
- Zhejiang PublicWelfare Technology Research Program [LGG19F030012]
- Scientific Research Project of Education Department of Zhejiang [Y201840830]
- National Natural Science Foundation of China [61603339]
- Zhejiang Xinmiao Talents Program [2019R403073]
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Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.
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