Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm

Short-term traffic flow forecasting is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a...

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Published inDiscrete dynamics in nature and society Vol. 2018; no. 2018; pp. 1 - 10
Main Authors Pan, Fuquan, Chen, Xiufeng, Qu, Dayi, Bing, Qichun, Wei, Jinli
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Analysis
Forecasting
Genetic algorithms
Kernel functions
Lagrange multiplier
Mathematical models
Mathematical optimization
Methods
Mutation
Neural networks
Optimization algorithms
Researchers
Short term
Time series
Traffic control
Traffic flow
Traffic management
Traffic regulations
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Summary:Short-term traffic flow forecasting is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. Firstly, the LSSVM model is constructed with combined kernel function. Then the GA-PSO hybrid optimization algorithm is designed to optimize the kernel function parameters efficiently and effectively. Finally, case validation is carried out using inductive loop data collected from the north-south viaduct in Shanghai. The experimental results demonstrate that the proposed GA-PSO-LSSVM model is superior to comparative method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1026-0226
1607-887X
DOI:10.1155/2018/3093596