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

Author:

Shuai Chunyan1,Pan Zhengyang1,Gao Lun1,Zuo HongWu2ORCID

Affiliation:

1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, China

2. School of Continuing Education, Kunming University of Science and Technology, Kunming, Yunnan 650093, China

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.

Publisher

Hindawi Limited

Subject

Civil and Structural Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Survey of short-term traffic flow prediction based on LSTM;International Journal of Modern Physics C;2024-07-09

2. Forecasting of Traffic Flow Using Feature Selection with ML Model;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28

3. Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM;IEEE Access;2023

4. Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM;Journal of Advanced Transportation;2022-09-21

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