A Genetic-Algorithm-based neural network approach for short-term traffic flow forecasting

Mingzhe Liu; Ruili Wang; Jiansheng Wu; Ray Kemp

doi:10.1007/11427469_152

A Genetic-Algorithm-based neural network approach for short-term traffic flow forecasting

Mingzhe Liu, Ruili Wang, Jiansheng Wu, Ray Kemp

Research output: Journal Publication › Conference article › peer-review

11 Citations (Scopus)

Abstract

In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.

Original language	English
Pages (from-to)	965-970
Number of pages	6
Journal	Lecture Notes in Computer Science
Volume	3498
Issue number	III
DOIs	https://doi.org/10.1007/11427469_152
Publication status	Published - 2005
Externally published	Yes
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: 30 May 2005 → 1 Jun 2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/11427469_152

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title = "A Genetic-Algorithm-based neural network approach for short-term traffic flow forecasting",

abstract = "In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.",

author = "Mingzhe Liu and Ruili Wang and Jiansheng Wu and Ray Kemp",

year = "2005",

doi = "10.1007/11427469_152",

language = "English",

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journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Verlag",

number = "III",

note = "Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 ; Conference date: 30-05-2005 Through 01-06-2005",

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TY - JOUR

T1 - A Genetic-Algorithm-based neural network approach for short-term traffic flow forecasting

AU - Liu, Mingzhe

AU - Wang, Ruili

AU - Wu, Jiansheng

AU - Kemp, Ray

PY - 2005

Y1 - 2005

N2 - In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.

AB - In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.

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U2 - 10.1007/11427469_152

DO - 10.1007/11427469_152

M3 - Conference article

AN - SCOPUS:24944545979

SN - 0302-9743

VL - 3498

SP - 965

EP - 970

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - III

T2 - Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

Y2 - 30 May 2005 through 1 June 2005

ER -

A Genetic-Algorithm-based neural network approach for short-term traffic flow forecasting

Abstract

ASJC Scopus subject areas

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