Huang Xiaohui, Jiang Yuan, Wang Junyang, Lan Yuanchun, Chen Huapeng
School of Information Engineering, East China Jiaotong University, Nanchang, 330200, China.
School of Civil Engineering, East China Jiaotong University, Nanchang, 330200, China.
Sci Rep. 2023 Dec 9;13(1):21859. doi: 10.1038/s41598-023-48579-3.
Accurate 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.
准确的交通流预测信息可以帮助交通管理者和驾驶员做出更合理的决策和选择。为了进行有效且准确的交通流预测，我们不仅需要考虑数据之间的时空依赖性，还需要考虑数据之间的时间相关性。然而，大多数现有方法只考虑时间连续性而忽略了时间相关性。在本文中，我们通过捕捉长短期序列相关性（LSTSC）提出了一种用于交通流预测的多模态注意力神经网络。在该模型中，我们采用注意力机制来捕捉序列的时空相关性，并且基于多种决策形式的模型表现出了更高的准确性和可靠性。该模型的优越性在两个数据集PeMS08和PeMSD7(M)上得到了证明，特别是对于长期预测。
