Traffic Prediction Using Graph Convolution Networks
Keywords:
Traffic prediction, Graph convolutional network (GCN), Deep learning, Spatial-temporal data, Time series forecasting, Hyperparameter optimization, Model evaluation, Accuracy, Precision, Recall, F1 score, Root mean squared error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE).Abstract
Traffic Prediction has become very essential now a days. It is helpful to manage the traffic in some major cities. In reality, the traffic data generated contains some missing values, due to communication errors, sensor faults, etc. Since the traffic data is Spatial-Temporal, which is very complex, it becomes difficult to handle these situations. So, the authors propose a Graph Convolution Network-based model to predict future traffic with missing values. The missing value processing and traffic prediction are processed in one step. The model considers both Spatial-Temporal data and the history of the particular junction traffic. The computational time complexity of the network is optimized for predicting the traffic.
References
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