Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network

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Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Reference [124] also reported the investigation on short-term traffic flow prediction.”
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017, result): “To overcome these drawbacks, a special recurrent NN named LSTM is proposed to forecast traffic speed and traffic flow (X. Ma [36], Y. Tian [37], Y. Chen [38], R. Fu [39]); the results indicate that LSTMs outperform MLP and SVMs.”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, methodology): “In the field of smart traffic, Tian and Pan [235] propose the use of RNN for traffic flow prediction.”
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, background): “Pan et al. [69] modeled road Authorized licensed use limited to: RMIT University Library.”
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020, methodology): “To extract temporal patterns, similar to object tracking in video, some advanced methods [22, 18, 13, 14] either employ recurrent neural networks or residual based periodic sampling techniques [4, 5] to learn temporal features.”