In view of the uncertainties in short-time traffic flows and the multimode correlation of traffic flow data, a grey prediction model for short-time traffic flows based on tensor decomposition is proposed. First, traffic flow data are expressed as tensors based on the multimode characteristics of traffic flow data, and the principle of the tensor decomposition algorithm is introduced. Second, the Verhulst model is a classic grey prediction model that can effectively predict saturated S-type data, but traffic flow data do not have saturated S-type data. Therefore, the tensor decomposition algorithm is applied to the Verhulst model, and then, the Verhulst model of the tensor decomposition algorithm is established. Finally, the new model is applied to short-term traffic flow prediction, and an instance analysis shows that the model can deeply excavate the multimode correlation of traffic flow data. At the same time, the effect of the new model is superior to five other grey prediction models. The predicted results can provide intelligent transportation system planning, control and optimization with reliable real-time dynamic information in a timely manner.

中文翻译：

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基于张量分解算法的短期交通流量预测研究

针对短时交通流的不确定性和交通流数据的多模相关性，提出一种基于张量分解的短时交通流灰色预测模型。首先，基于交通流数据的多模特征，将交通流数据表示为张量，并介绍了张量分解算法的原理。其次，Verhulst模型是经典的灰色预测模型，可以有效地预测饱和的S型数据，但是交通流数据没有饱和的S型数据。因此，将张量分解算法应用于Verhulst模型，然后建立张量分解算法的Verhulst模型。最后，将新模型应用于短期交通流量预测，实例分析表明，该模型可以深入挖掘交通流量数据的多模相关性。同时，新模型的效果优于其他五个灰色预测模型。预测结果可以及时提供具有可靠实时动态信息的智能交通系统规划，控制和优化。