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Accurate traffic prediction can help residents plan travel routes, relieve traffic congestion, and reduce traffic accidents. At present, most traffic flow prediction models do not fully consider the spatiotemporal dependence of traffic flow data, and cannot explore the deep temporal and spatial correlation of traffic flow. Therefore, this paper proposes a traffic flow prediction model based on ConvLSTM and fuzzy clustering (i.e. ConvLSTM-GK model). The model is structured in feature generation module, ConvLSTM module and GK cluster analysis module. The feature generation module is used for the construction of data features; the ConvLSTM module can fully integrate the spatiotemporal information to make the feature extraction effect better, the one-dimensional convolution in ConvLSTM is used to extract spatial local information, and LSTM is used to extract time series information; according to the similarity between the data to be predicted and the training data of the ConvLSTM module, the GK Cluster Analysis module estimates the error of the data to be predicted based on the similarity between the data to be predicted and the training data of the ConvLSTM module, and compensates for the error of the prediction results of the ConvLSTM module to improve the prediction accuracy of the model. Experimental results show that under the training of PeMS dataset, the prediction accuracy of ConvLSTM-GK model in mean absolute error (MAE) and mean absolute percentage error (MAPE) is 14.26 vehicles/5 minutes and 17.77%, respectively, which is higher than that of LSTM, CNN-LSTM, ConvLSTM and AT-Conv-LSTM models, which proves that the ConvLSTM-GK model has performance advantages in traffic flow prediction tasks.
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