Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

Chunyan Shuai,Zhengyang Pan,Hongwu Zuo,Lun Gao,Xiuling Wang

doi:10.1155/2021/4313970

Chunyan Shuai, Zhengyang Pan + Show 3 more

Open Access

https://doi.org/10.1155/2021/4313970

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Abstract

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.

Highlights

Expressway traffic flow prediction and management is always an important research field of intelligent transportation [1]
Considering the complexity characteristics of the traffic flow, this paper proposes a hybrid model (SSA-Long Short-Term Memory (LSTM)-Support Vector Regression (SVR)), which decomposes the flow into different components and, according to their respective features, employs different models to make forecasting
Due to the inherent complexity of expressway traffic flow, the prediction accuracy of a single model is limited; the idea of combined prediction model is proposed in this paper

Summary

Introduction

Expressway traffic flow prediction and management is always an important research field of intelligent transportation [1]. To solve the problem of SARIMA requiring a sound data for model building, Kumar and Vanajakshi [3] made the traffic flow a stationary one by differencing and used the autocorrelation function, partial autocorrelation function, and maximum likelihood method to identify the suitable parameters of the SARIMA model and made traffic flow forecast with limited data. Considering their limitations of the ARIMA needing the stationarity and autocorrelation of the time series and the variability of traffic flow, the ARIMA family is usually combined with other nonlinear models to make prediction of flow. Hou et al [5] proposed an adaptive hybrid model, in which ARIMA captures the linear laws and the Wavelet Neural Network method obtains the nonlinear

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