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

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...

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Main Authors: Chunyan Shuai, Zhengyang Pan, Lun Gao, HongWu Zuo
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Advances in Civil Engineering
Online Access:http://dx.doi.org/10.1155/2021/4313970
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author Chunyan Shuai
Zhengyang Pan
Lun Gao
HongWu Zuo
author_facet Chunyan Shuai
Zhengyang Pan
Lun Gao
HongWu Zuo
author_sort Chunyan Shuai
collection DOAJ
description 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.
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institution Kabale University
issn 1687-8086
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publishDate 2021-01-01
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series Advances in Civil Engineering
spelling doaj-art-f891170aacb54b9f9c40d642e4891a722025-02-03T01:24:54ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/43139704313970Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis DecompositionChunyan Shuai0Zhengyang Pan1Lun Gao2HongWu Zuo3Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Continuing Education, Kunming University of Science and Technology, Kunming, Yunnan 650093, ChinaReal-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.http://dx.doi.org/10.1155/2021/4313970
spellingShingle Chunyan Shuai
Zhengyang Pan
Lun Gao
HongWu Zuo
Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
Advances in Civil Engineering
title Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
title_full Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
title_fullStr Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
title_full_unstemmed Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
title_short Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition
title_sort short term traffic flow prediction of expressway a hybrid method based on singular spectrum analysis decomposition
url http://dx.doi.org/10.1155/2021/4313970
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AT lungao shorttermtrafficflowpredictionofexpresswayahybridmethodbasedonsingularspectrumanalysisdecomposition
AT hongwuzuo shorttermtrafficflowpredictionofexpresswayahybridmethodbasedonsingularspectrumanalysisdecomposition