Author:
Huang Xiaohui,Jiang Yuan,Wang Junyang,Lan Yuanchun,Chen Huapeng
Abstract
AbstractAccurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Natural Science Foundation ofJiangxi Province
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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