Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt

  • Lida Barba
  • Nibaldo Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)

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 a MAPE 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.

Keywords

Autoregressive Neural Network Levenberg-Marquardt Singular Value Decomposition 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lida Barba
    • 1
    • 2
  • Nibaldo Rodríguez
    • 2
  1. 1.Engineering FacultyUniversidad Nacional de ChimborazoRiobambaEcuador
  2. 2.School of Informatics EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile

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