Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.
A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine
