Adaptive hyperparameter optimization for short term traffic flow prediction with spatial temporal correlated raster data

TL;DR: Experiments show that the proposed ISTGCN model effectively captures spatial-temporal information and improves traffic prediction accuracy with the state-of-the-art model, and the training time is substantially reduced due to the smaller number of training parameters.

TL;DR: Federated Learning is adopted to train the prediction model closer to the data sources for traffic prediction in Arhus City, and the method's prediction accuracy has been evaluated compared to a centralized approach.

TL;DR: This study proposes the multi-scaling hybrid model (MSHM) for fine-grained traffic flow-based travel time estimation prediction, addressing inter-grid transitions and dynamic temporal dependencies using multi-directional convolutional layers and enhanced deep super-resolution techniques.

TL;DR: This paper uses Long Short Term Memory and Gated Recurrent Units (GRU) neural network methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model.

TL;DR: This paper proposes a CNN-LSTM neural network that can extract spatial and temporal features to effectively predict the housing energy consumption and achieves almost perfect prediction performance for electric energy consumption that was previously difficult to predict.

TL;DR: The results indicate that ARIMAX models provide improved forecast performance over univariate forecast models, but further research is needed to investigate model extensions and refinements to provide a generalizable, self-tuning multivariate forecasting model that is easily implemented and that effectively models varying upstream to downstream correlations.

TL;DR: The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that theImproved KNN model is more appropriate for short-term traffic multistep forecasting than theother models are.