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Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features
Applied Intelligence ( IF 5.3 ) Pub Date : 2021-06-05 , DOI: 10.1007/s10489-021-02461-9
Chunhui Xu , Anqin Zhang , Chunchen Xu , Yu Chen

With the more and more in-depth research on intelligent transportation, many scholars have proposed their models for accurate prediction of traffic. In this paper, we analyze the advantages and disadvantages of the existing models and propose our own model. In our model, the temporal and spatial factors are taken into account. Gate Recurrent Unit (GRU) and Gated Linear Units (GLU) are used to learn the short-term temporal features of traffic data, and Graph Convolutional Network (GCN) is used to learn the spatial features of traffic data. In order to fully learn short-term feature changes, a multi time step perception layer is proposed. A new network GCGRU is proposed to learn the long-term features of traffic data. As the sensor will be affected by urban canyon, weather, and other factors, there will be missing value and noise in the collected data. We created a short-term trend based missing value filling up algorithm to fill in missing values and use Singular Spectrum Analysis (SSA) algorithm to eliminate noise of training data set. In order to reduce the process of adjusting parameters manually in the model training process, we propose k-block search method based on fuzzy extreme points. Finally, the model is compared with the existing traffic forecasting models, and the analysis results show that our model has advantages in many indicators.



中文翻译:

交通速度预测:基于长期、短期和空间特征的时空卷积网络

随着对智能交通的研究越来越深入,许多学者提出了他们的交通准确预测模型。在本文中,我们分析了现有模型的优缺点并提出了我们自己的模型。在我们的模型中,考虑了时间和空间因素。门循环单元(GRU)和门控线性单元(GLU)用于学习交通数据的短期时间特征,图卷积网络(GCN)用于学习交通数据的空间特征。为了充分学习短期特征变化,提出了多时间步感知层。提出了一种新的网络 GCGRU 来学习交通数据的长期特征。由于传感器会受到城市峡谷、天气等因素的影响,收集的数据中会有缺失值和噪声。我们创建了一种基于短期趋势的缺失值填充算法来填充缺失值,并使用奇异谱分析(SSA)算法来消除训练数据集的噪声。为了减少模型训练过程中手动调整参数的过程,我们提出了基于模糊极值点的k块搜索方法。最后将模型与现有的交通预测模型进行对比,分析结果表明我们的模型在很多指标上都具有优势。我们提出了基于模糊极值点的k块搜索方法。最后将模型与现有的交通预测模型进行对比,分析结果表明我们的模型在很多指标上都具有优势。我们提出了基于模糊极值点的k块搜索方法。最后将模型与现有的交通预测模型进行对比,分析结果表明我们的模型在很多指标上都具有优势。

更新日期:2021-06-05
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