A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction

Abstract

Deep learning techniques have been widely used in traffic flow prediction. They can perform much better than shallow models. However, most existing deep learning models only focus on deterministic data, ignoring the fact that traffic flow contains a large amount of uncertain data. Therefore, this paper proposes a novel hybrid model called FGRU combining a fuzzy inference system (FIS) and a gated recurrent unit (GRU) neural network to predict short-term traffic flows. The GRU model is applied to capture the temporal dependencies within traffic flow data, and the FIS makes up for the shortcomings of deep learning by lessening the influence of uncertain data. In addition, a temporal feature enhancement mechanism is proposed to calculate the appropriate time intervals as model inputs. The most appropriate model structure and parameters are explored by performing comparative experiments. Finally, the simulation results show that the mean absolute error of FGRU is 7.75% and 3.05% lower than ARIMA and the state-of-the-art traffic flow prediction model based on deep learning.