Traffic flow forecasting based on grey neural network model

Shu Yan Chen*, Gao Feng Qu, Xing He Wang, Huai Zhong Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

14 Citations (Scopus)

Abstract

In this paper, a kind of Grey Neural Network (abbreviate as GNN) is proposed which combines grey system theory with neural network, that is, the GNN model has been built by adding a grey layer before neural input layer and a white layer after neural output layer. Gray neural network can elaborate advantages of both grey model and neural network, and enhance further precision of forecasting. The GNN model is employed to forecast a real vehicle traffic flow of JINGSHI highway with favor precision and result, which is firstly applied GNN to traffic flow forecasting. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GNN model is outperformed GM model and neural network model, and traffic flow forecasting based on GNN is of validity and feasibility.

Original languageEnglish
Title of host publicationInternational Conference on Machine Learning and Cybernetics
Pages1275-1278
Number of pages4
Publication statusPublished - 2003
EventInternational Conference on Machine Learning and Cybernetics - Xi'an, China
Duration: 2 Nov 20035 Nov 2003

Publication series

NameInternational Conference on Machine Learning and Cybernetics
Volume2

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityXi'an
Period2/11/035/11/03

Keywords

  • Forecasting
  • Grey Neural Network
  • Traffic Flow

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