Fine-Tuning Deep Hybrid Long Short-Term Memory and Restricted Boltzmann Machine Network for Urban Traffic Speed Prediction

Abstract

Accurate urban traffic speed prediction plays a crucial role in intelligent transportation systems (ITSs). However, the potential spatial-temporal information of big urban traffic data captured from the complex behaviors of urban systems cannot be efficiently mined based on the state-of-art intelligent models. Therefore, we propose a hybrid Long Short-Term Memory (LSTM) and Restricted Boltzmann Machine (RBM) neural network with a fine-tuning strategy for urban traffic speed prediction. Firstly, the LSTM-RBM model dynamically combines the LSTM and RBM, such that the LSTM extracts the time-varying characteristics of the speed sequences to dynamically adjust the RBM, and then the RBM can capture the deep and detailed features of the speed sequences in the optimal way. Secondly, a transfer learning fine-tuning strategy is proposed to effectively pre-train the LSTM-RBM to achieve higher accuracy. Experimental results based on traffic speed data of the second ring road in Xi'an indicate that the proposed hybrid LSTM-RBM model with fine-tuning can outperform the existing deep models.