Triplet Decoders Neural Network Ensemble System and T-Conversion for Traffic Speed Sequence Prediction

Abstract

Traffic flow prediction is essential in Intelligent Transportation System (ITS). Due to the complexity of the traffic environment, predicting future traffic speed is difficult. A lot of methods have been proposed to forecast traffic flow, such as support vector regression (SVR), recurrent neural networks (RNN), and so on. However, there are few ensemble models methods for predicting traffic flow, especially for predicting multi-step traffic speed. Meanwhile, these ensemble methods often use many base models for regression. Thus, the performances of these existing methods are worth exploring for multi-step traffic forecasts with few base models. Besides, this paper proposes a model based on ensemble models, called “Triplet Decoders Neural Network” (TDNN), to compare with these methods. By analyzing the characteristic of traffic speed data, this paper also proposes a data preprocessing model called “T-Conversion” to help RNN capturing long-term dependencies. Our experiments are implemented based on real traffic speed data from Baidu in Beijing, China because the prediction for complex traffic is more valuable. With these new novel ideas, our experiments show the superior performances of TDNN, and the significant effect of T-Conversion for predicting traffic speed sequences.

Keywords

• Recurrent neural network (RNN)
• long short term memory (LSTM)
• gated recurrent unit (GRU)
• sequence to sequence (Seq2Seq)
• ensemble models