T2F-LSTM Method for Long-Term Traffic Volume Prediction

Abstract

Long short-term memory (LSTM) neural network shows excellent performance in learning, processing, and classifying time series data but with some limitations such as high computational cost and lack of interpretability. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy system, thus, constitute a more promising technique for processing traffic flow. This article presents a Type-2 fuzzy LSTM (T2F-LSTM) neural network model for long-term traffic volume prediction. T2F Sets (T2FSs) provide more freedom to describe membership information and process data with higher uncertainty better than the traditional fuzzy system does. In this article, an interval T2FSs is introduced to extract the probability distribution and spatial–temporal characteristics of traffic volume. Using parameters of the closure of support obtained in interval T2FSs, weights of input gate in LSTM neural network are updated and converged to the region with a larger slope of the sigmoid function fast. The network interpretability is also increased by better control of the information flow using motivational factors constructed from the parameters. Experiment conducted with real traffic volume data shows that the proposed model achieves more accurate prediction results and shorter network training time.