The invention relates to a traffic flow prediction method of a deep network based on fusion of spatiotemporal features. The method comprises the following steps: step A, acquiring historical traffic flow data containing spatiotemporal information from a traffic platform; step B, preprocessing the historical traffic flow data containing the spatiotemporal information to obtain space-time matrix representation of the historical traffic flow data; step C, training a deep learning network TSNN by taking the space-time matrix representation as the input of the deep learning network; and step D, inputting a traffic flow data sequence to be predicted into the trained deep learning network to obtain a prediction result. According to the method, the problems of incomplete feature extraction and incomplete feature fusion in traffic flow prediction are solved, and the accuracy and the precision of traffic flow prediction are improved.
本发明涉及一种基于融合时空特征的深度网络的交通流预测方法,包括以下步骤:步骤A:从交通平台获取包含时空信息的历史交通流数据;步骤B:对包含时空信息的历史交通流数据进行预处理,得到历史交通流数据的时空矩阵表征;步骤C:以时空矩阵表征作为深度学习网络的输入,训练深度学习网络TSNN;步骤D:将待预测的交通流数据序列输入到训练好的深度学习网络中,得到预测结果。本发明解决了交通流预测中特征提取不完整和特征融合不完全的问题,该方法提高了交通流预测的准确度和精度。
Traffic flow prediction method of deep network based on fusion of spatiotemporal features
基于融合时空特征的深度网络的交通流预测方法
2020-06-05
Patent
Electronic Resource
Chinese
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