ArticlePloS one2016
A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Traffic flow prediction based on improved deep extreme learning machine.Scientific reports · 2025Article
- Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation.Frontiers in plant science · 2023Article
- Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting.Sensors (Basel, Switzerland) · 2022Review
- An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic.Sensors (Basel, Switzerland) · 2021Article
- Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm.Mobile networks and applications : MONET · 2021Article
- Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
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Abstract
Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.