Traffic flow prediction method fusing spatial and temporal features

The invention discloses a traffic flow prediction method fusing spatial and temporal features. The method comprises the following steps: step 1, preprocessing data; step 2, introducing an automatic encoder to obtain data characteristics; step 3, introducing an SAEs model and acquiring spatial feature ; step 4, introducing an LSTM model, and obtaining time features; step 5, synthesizing the SAEs model and the LSTM model to obtain an ideal hybrid model, and establishing a hybrid deep learning model SAES-LSTM to predict the traffic flow of an urban expressway. Time and space information is comprehensively utilized. The collected information of a database is analyzed and utilized more fully, and therefore a prediction result can be more accurate. 一种融合时空特征的交通流预测方法，包括以下步骤：步骤1，先对数据进行预处理；步骤2，引入自动编码器得到数据特征；步骤3，引入SAEs模型，获取空间特征；步骤4，引入LSTM模型，获取时间特征；步骤5，将SAEs模型与LSTM模型综合起来得到理想的混合模型，建立一种混合深度学习模型SAEs-LSTM来预测城市高速公路的交通流。本发明模型综合利用了时间与空间信息，对已收集到的数据库的信息分析利用得更充分，从而预测结果能更精准。