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 featur...
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Format: | Patent |
Sprache: | chi ; eng |
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Zusammenfassung: | 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。 |
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