The invention discloses an expressway short-time traffic flow prediction method based on deep learning. The method comprises the following steps: firstly, acquiring traffic flow data of a plurality of sections detected by a radar in real time, performing preprocessing by combining historical and real-time traffic flow data, and dividing traffic modes into three modes of workdays, dual-holidays and holidays according to periodic characteristics of the traffic flow data; fourier transform is used to extract time features of the traffic flow data, an attention layer is added to extract space features of the multi-section traffic flow data, and then a gating circulation unit time sequence prediction neural network model is constructed; and finally, performing model training and prediction by using the spatial-temporal characteristics of the same traffic mode, and verifying the accuracy of a prediction result. According to the method, the traffic flow data time features and the multi-section space features are fully extracted, prediction is carried out based on the single step length and the multi-step length, short-time traffic flow high-precision prediction is achieved, and the method is suitable for expressway main lines and arterial highways.
本发明公开了一种基于深度学习的高速公路短时交通流预测方法。本发明首先获取雷达实时检测的多个断面交通流数据,结合历史与实时交通流数据进行预处理,并根据交通流数据的周期特征,划分交通模式为工作日、双休日和节假日三种模式;然后使用傅里叶变换提取交通流数据的时间特征,通过添加注意力层提取多断面交通流数据的空间特征,进而构建门控循环单元时序预测神经网络模型;最后使用同一交通模式的时空特征进行模型训练与预测,并验证了预测结果的准确度。本发明充分提取交通流数据时间特征与多断面空间特征,并基于单步长与多步长进行预测,实现短时交通流高精度预测,该方法适用于高速公路主线及干线公路。
Highway short-time traffic flow prediction method based on deep learning
一种基于深度学习的高速公路短时交通流预测方法
2023-04-04
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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