Features injected recurrent neural networks for short-term traffic speed prediction

Abstract

Accurate traffic speed forecasting is critical in advanced transportation management and traveler route planing. Considering the important influences of spatial–temporal factors and excellent performance of recurrent neural networks (RNNs) in the field of time series analyzing, in this paper, the features injected recurrent neural networks (FI-RNNs) were proposed, which combines sequential time data with contextual factors to mine the potential relationship between traffic state and its context. In this model, a stacked RNN was used to learn the sequence features of traffic data. Meanwhile, a sparse Autoencoder was trained to expand the contextual features, which are high-level coding and abstract representations of contextual factors. Then an merging mechanism which injects contextual features into sequence features was explored to generate fusion features. Finally, the new fused features were fed to the predictor to learn the traffic patterns and predict future speed. Case studies based on two real-world data sets show that the injection of contextual features can greatly improve the accuracy of time series prediction. Comparison with ten frequently used models, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree (DT), gradient booting decision tree (GBDT), random forest (RF), stacked autoencoder (SAE), and four classic RNNs, also shows the proposed models outperform these state-of-the-art traffic prediction methods in terms of accuracy and stability.