Huang Jiani, Yan He, Chen Qixiu, Liu Yingan
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2024 Dec 19;24(24):8106. doi: 10.3390/s24248106.
Traffic flow forecasting is integral to transportation to avoid traffic accidents and congestion. Due to the heterogeneous and nonlinear nature of the data, traffic flow prediction is facing challenges. Existing models only utilize plain historical data for prediction. Inadequate use of temporal information has become a key problem in current forecasting. To address the problem, we must effectively analyze the influence of time periods while integrating the distinct characteristics of traffic flow across various time granularities. This paper proposed a multi-granularity temporal embedding Transformer network, namely MGTEFormer. An embedding input adeptly merges complex temporal embeddings, a temporal encoder to consolidate rich temporal information, and a spatial encoder to discern inherent spatial characteristics between different sensors. The combined embeddings are fed into the attention mechanism's encoder, culminating in prediction results obtained through a linear regression layer. Temporal embedding can help by fussing the period and various temporal granularities into plain historical traffic flow that can be learned adequately, reducing the loss of time information. Experimental analyses and ablation studies conducted on real traffic datasets consistently attest to the superior performance of the MGTEFormer. Our approach reduces the mean absolute error of the original models by less than 1.7%. Extensive experiments demonstrate the superiority of the proposed MGTEFormer over existing benchmarks.
交通流预测对于交通运输至关重要，有助于避免交通事故和拥堵。由于数据具有异质性和非线性，交通流预测面临着挑战。现有模型仅利用简单的历史数据进行预测。对时间信息利用不足已成为当前预测中的关键问题。为解决该问题，我们必须在整合不同时间粒度下交通流独特特征的同时，有效分析时间段的影响。本文提出了一种多粒度时间嵌入Transformer网络，即MGTEFormer。一个嵌入输入巧妙地融合了复杂的时间嵌入，一个时间编码器用于整合丰富的时间信息，以及一个空间编码器用于识别不同传感器之间的内在空间特征。组合后的嵌入被输入到注意力机制的编码器中，最终通过线性回归层获得预测结果。时间嵌入通过将时间段和各种时间粒度融入到可充分学习的简单历史交通流中发挥作用，减少时间信息的损失。在真实交通数据集上进行的实验分析和消融研究一致证明了MGTEFormer的卓越性能。我们的方法将原始模型的平均绝对误差降低了不到1.7%。大量实验证明了所提出的MGTEFormer优于现有基准。
