The invention discloses a multi-modal traffic flow prediction method based on multi-source data feature fusion. The method comprises the following steps: acquiring traffic data; dividing a training set and a test set according to the traffic data; constructing a traffic prediction model, and training the traffic prediction model by adopting the training set; verifying the traffic prediction model by using the test set; the traffic prediction model comprises a cloud image encoder used for carrying out feature extraction on cloud image data to obtain cloud image features; the space-time encoder is used for performing feature extraction on the space-time data to obtain space-time features; the fusion module is used for fusing the cloud image features and the spatial-temporal features to obtain fusion features; and carrying out addition operation on the cloud picture features, the spatial-temporal features and the fusion features, and inputting into a spatial-temporal decoder for prediction to obtain a final prediction result. According to the method, multi-azimuth and multi-dimensional information is fused, redundant information in the spatial-temporal feature extraction process is simplified, efficient and high-precision prediction is achieved, and the method can be widely applied to the technical field of traffic flow prediction.
本发明公开了一种基于多源数据特征融合的多模态交通流量预测方法,包括:获取交通数据;根据交通数据划分训练集和测试集;构建交通预测模型,采用训练集对交通预测模型进行训练;采用测试集对交通预测模型进行验证;其中,交通预测模型包括:云图编码器,用于对云图数据进行特征提取,获得云图特征;时空编码器,用于对时空数据进行特征提取,获得时空特征;融合模块,用于将云图特征和时空特征进行融合,获得融合特征;将云图特征、时空特征和融合特征进行相加操作,输入时空解码器进行预测,获得最终的预测结果。本发明融合多方位多维度信息,精简时空特征提取过程中的冗余信息,实现了高效且高精度的预测,可广泛应用于交通流量预测技术领域。
Multi-modal traffic flow prediction method based on multi-source data feature fusion
一种基于多源数据特征融合的多模态交通流量预测方法
2025-01-17
Patent
Electronic Resource
Chinese
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