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Smart city article details

Title	Urban Short - Term Traffic Flow Prediction Algorithm Based On Cnn-Lstm Model
ID_Doc	60175
Authors	Zhang X.; Huang K.; Liu C.; Xu X.
Year	2023
Published	2023 3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023
DOI	http://dx.doi.org/10.1109/ICCECE58074.2023.10135384
Abstract	With the continuous advancement of smart city construction, urban short-term traffic flow forecasting becomes more and more important. According to the influence of traffic flow characteristics and external factors on traffic flow forecast results, the model CNN-LSTM for urban short-term traffic flow forecasting is proposed. The model integrates convolutio nal neural networks(CNN)and long-short-term memory networks (LSTM) into an end-to-end network framework. The convolutional neural network is used to capture the local spatial characteristics of traffic flow. On the other hand, the long short-term memory-cycle neural network is used to capture temporal characteristics of traffic flow data. The output results of the two networks are combined by the corresponding weights to obtain the predicted results through the trajectory data. Finally, the traffic flow prediction values of the urban areas are obtained by fusing with external factors. Through the verification of the CNN-LSTM model by Kunming data, the model is not only higher in accuracy than the traditional model, but also has fewer parameters in the case of ensuring the accuracy of prediction. © 2023 IEEE.
Author Keywords	convolutional neural network; deep learning; long short-term memory network; traffic flow forecasting

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