Traffic data analysis using deep Elman and gated recurrent auto-encoder

Traffic flow prediction is one of the most interesting machine learning applications in real-world problems that can help anyone move around. In this study, we proposed a feature extraction structure for multivariate time series using Elman recurrent auto-encoder. We added loopback from the encoder layer of the normal auto-encoder to regard sequence information between successive data. The feedback layer implemented using Elman neural network and GRU cells, then the model is trained by different optimization algorithms. The models are also trained using the Emotional Learning method in which we involve the derivative of the error in the cost function to avoid local minimums and keep the last state of the network. We used the proposed method for classification and prediction problems on traffic data from the California Department of Transportation Performance Measurement System (PeMS). The results show that our structure can successfully extract a compact representation of traffic data useful for reconstructing of original data, classification, and prediction. The results also show that adding the recurrent layer to the feature extractor (auto-encoder) leads to better results in the classification phase in comparison with standard methods that do not use the recurrence during feature extraction.