Combination prediction for short-term traffic flow based on artificial neural network

Liu Jiansheng; Fu Hui; Liao Xinxing

doi:10.1109/WCICA.2006.1713671

Combination prediction for short-term traffic flow based on artificial neural network

Liu Jiansheng^*
, Fu Hui
, Liao Xinxing

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

As the basis of urban traffic control and guidance, the prediction for short-term traffic flow is constrained by its dynamic properties. To build an optimum model and enhance the predicting accuracy of the traffic flow, a combination prediction algorithm based on neural network is proposed. According to the algorithm, the first Lyapunov exponent and recurrence plot are used to analyze the forecasting property of a traffic flow, and a set of predicting models are determined corresponding to the analysis. The predicted results of the traffic flow are obtained by a nonlinear combination model based on a neural network. Both simulated and real detected traffic volume are used to verify the effectiveness of the algorithm.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	8659-8663
Number of pages	5
DOIs	https://doi.org/10.1109/WCICA.2006.1713671
State	Published - 2006
Externally published	Yes
Event	6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	2

Conference

Conference	6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/Territory	China
City	Dalian
Period	21/06/06 → 23/06/06

Keywords

Artificial neural network
Combinatorial prediction
Short-term traffic flow

Access to Document

10.1109/WCICA.2006.1713671

Cite this

Jiansheng, L., Hui, F., & Xinxing, L. (2006). Combination prediction for short-term traffic flow based on artificial neural network. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 8659-8663). Article 1713671 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2). https://doi.org/10.1109/WCICA.2006.1713671

@inproceedings{1305349e779349c79be4a9371eb4c665,

title = "Combination prediction for short-term traffic flow based on artificial neural network",

abstract = "As the basis of urban traffic control and guidance, the prediction for short-term traffic flow is constrained by its dynamic properties. To build an optimum model and enhance the predicting accuracy of the traffic flow, a combination prediction algorithm based on neural network is proposed. According to the algorithm, the first Lyapunov exponent and recurrence plot are used to analyze the forecasting property of a traffic flow, and a set of predicting models are determined corresponding to the analysis. The predicted results of the traffic flow are obtained by a nonlinear combination model based on a neural network. Both simulated and real detected traffic volume are used to verify the effectiveness of the algorithm.",

keywords = "Artificial neural network, Combinatorial prediction, Short-term traffic flow",

author = "Liu Jiansheng and Fu Hui and Liao Xinxing",

year = "2006",

doi = "10.1109/WCICA.2006.1713671",

language = "英语",

isbn = "1424403324",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "8659--8663",

booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",

}

Jiansheng, L, Hui, F & Xinxing, L 2006, Combination prediction for short-term traffic flow based on artificial neural network. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA)., 1713671, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 2, pp. 8659-8663, 6th World Congress on Intelligent Control and Automation, WCICA 2006, Dalian, China, 21/06/06. https://doi.org/10.1109/WCICA.2006.1713671

Combination prediction for short-term traffic flow based on artificial neural network. / Jiansheng, Liu; Hui, Fu; Xinxing, Liao.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 8659-8663 1713671 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combination prediction for short-term traffic flow based on artificial neural network

AU - Jiansheng, Liu

AU - Hui, Fu

AU - Xinxing, Liao

PY - 2006

Y1 - 2006

N2 - As the basis of urban traffic control and guidance, the prediction for short-term traffic flow is constrained by its dynamic properties. To build an optimum model and enhance the predicting accuracy of the traffic flow, a combination prediction algorithm based on neural network is proposed. According to the algorithm, the first Lyapunov exponent and recurrence plot are used to analyze the forecasting property of a traffic flow, and a set of predicting models are determined corresponding to the analysis. The predicted results of the traffic flow are obtained by a nonlinear combination model based on a neural network. Both simulated and real detected traffic volume are used to verify the effectiveness of the algorithm.

AB - As the basis of urban traffic control and guidance, the prediction for short-term traffic flow is constrained by its dynamic properties. To build an optimum model and enhance the predicting accuracy of the traffic flow, a combination prediction algorithm based on neural network is proposed. According to the algorithm, the first Lyapunov exponent and recurrence plot are used to analyze the forecasting property of a traffic flow, and a set of predicting models are determined corresponding to the analysis. The predicted results of the traffic flow are obtained by a nonlinear combination model based on a neural network. Both simulated and real detected traffic volume are used to verify the effectiveness of the algorithm.

KW - Artificial neural network

KW - Combinatorial prediction

KW - Short-term traffic flow

UR - https://www.scopus.com/pages/publications/34047225293

U2 - 10.1109/WCICA.2006.1713671

DO - 10.1109/WCICA.2006.1713671

M3 - 会议稿件

AN - SCOPUS:34047225293

SN - 1424403324

SN - 9781424403325

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 8659

EP - 8663

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006

Y2 - 21 June 2006 through 23 June 2006

ER -

Combination prediction for short-term traffic flow based on artificial neural network

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this