Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt

Lida Barba; Nibaldo Rodríguez

doi:10.1007/978-3-319-13817-6_9

Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt

Lida Barba, Nibaldo Rodríguez

SCHOOL OF COMPUTER ENGINEERING

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

Abstract

In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved aMAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.

Original language	English
Title of host publication	Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings
Editors	Rajendra Prasath, Philip O’Reilly, Thangairulappan Kathirvalavakumar
Publisher	Springer Verlag
Pages	82-90
Number of pages	9
ISBN (Electronic)	9783319138169
DOIs	https://doi.org/10.1007/978-3-319-13817-6_9
State	Published - 2014
Event	2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014 - Cork, Ireland Duration: 10 Dec 2014 → 12 Dec 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8891
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014
Country/Territory	Ireland
City	Cork
Period	10/12/14 → 12/12/14

Keywords

Autoregressive Neural Network
Levenberg-Marquardt
Singular Value Decomposition

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-13817-6_9

Cite this

Barba, L., & Rodríguez, N. (2014). Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt. In R. Prasath, P. O’Reilly, & T. Kathirvalavakumar (Eds.), Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings (pp. 82-90). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8891). Springer Verlag. https://doi.org/10.1007/978-3-319-13817-6_9

Barba, Lida ; Rodríguez, Nibaldo. / Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt. Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings. editor / Rajendra Prasath ; Philip O’Reilly ; Thangairulappan Kathirvalavakumar. Springer Verlag, 2014. pp. 82-90 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt",

abstract = "In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepci{\'o}n, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved aMAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.",

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author = "Lida Barba and Nibaldo Rodr{\'i}guez",

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Barba, L & Rodríguez, N 2014, Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt. in R Prasath, P O’Reilly & T Kathirvalavakumar (eds), Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8891, Springer Verlag, pp. 82-90, 2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014, Cork, Ireland, 10/12/14. https://doi.org/10.1007/978-3-319-13817-6_9

Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt. / Barba, Lida; Rodríguez, Nibaldo.
Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings. ed. / Rajendra Prasath; Philip O’Reilly; Thangairulappan Kathirvalavakumar. Springer Verlag, 2014. p. 82-90 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8891).

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

TY - GEN

T1 - Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt

AU - Barba, Lida

AU - Rodríguez, Nibaldo

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved aMAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.

AB - In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved aMAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.

KW - Autoregressive Neural Network

KW - Levenberg-Marquardt

KW - Singular Value Decomposition

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DO - 10.1007/978-3-319-13817-6_9

M3 - Conference contribution

AN - SCOPUS:84915785729

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 82

EP - 90

BT - Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings

A2 - Prasath, Rajendra

A2 - O’Reilly, Philip

A2 - Kathirvalavakumar, Thangairulappan

PB - Springer Verlag

T2 - 2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

Y2 - 10 December 2014 through 12 December 2014

ER -

Barba L, Rodríguez N. Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt. In Prasath R, O’Reilly P, Kathirvalavakumar T, editors, Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings. Springer Verlag. 2014. p. 82-90. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-13817-6_9

Forecast of traffic accidents based on components extraction and an autoregressive neural network with levenberg-marquardt

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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