Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on SpaceTime Analysis and GRU
Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on SpaceTime Analysis and GRU
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基于时空分析和GRU的城市路段短期交通流预测方法
DOI：
10.1109/access.2019.2941280
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Xu, Xuecai
中科院分区：
文献类型：
--
作者：
Dai, Guowen;Ma, Changxi;Xu, Xuecai
准确的短期交通预测有助于人们选择交通工具和出行时间。通过查询数据，很多交通流预测模型都忽略了交通流的时空相关性，使得预测精度受到交通数据精度的限制。提出了一种时空分析与GRU相结合的短时交通流预测模型。该模型首先对采集的交通流数据进行时间相关性分析和空间相关性分析，然后采用时空特征选择算法确定最佳输入时间间隔和空间数据量。同时，从实际交通流数据中提取出选定的交通流数据，并将其转换为具有时空交通流信息的二维矩阵。利用GRU对矩阵内部交通流的时空特征信息进行处理，达到预测的目的。最后，将该模型的预测结果与实际交通流数据进行对比，验证了模型的有效性。将该模型与卷积神经网络（CNN）模型和GRU模型进行了比较，结果表明，该方法在准确性和稳定性方面均优于CNN模型和GRU模型。
Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability.