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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationMining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings
EditorsRajendra Prasath, Philip O’Reilly, Thangairulappan Kathirvalavakumar
PublisherSpringer Verlag
Pages82-90
Number of pages9
ISBN (Electronic)9783319138169
StatePublished - 1 Jan 2014
Event2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014 - Cork, Ireland
Duration: 10 Dec 201412 Dec 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8891
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014
CountryIreland
CityCork
Period10/12/1412/12/14

Keywords

  • Autoregressive Neural Network
  • Levenberg-Marquardt
  • Singular Value Decomposition

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