Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO

Lida Barba, Nibaldo Rodríguez


In this paper, we propose a strategy to improve the forecasting of traffic accidents in Concepción, Chile. The forecasting strategy consists of four stages: embedding, decomposition, estimation and recomposition. At the first stage, the Hankel matrix is used to embed the original time series. At the second stage, the Singular Value Decomposition (SVD) technique is applied. SVD extracts the singular values and the singular vectors, which are used to obtain the components of low and high frequency. At the third stage, the estimation is implemented with an Autoregressive Neural Network (ANN) based on Particle Swarm Optimization (PSO). The final stage is recomposition, where the forecasted value is obtained. The results are compared with the values given by the conventional forecasting process. Our strategy shows high accuracy and is superior to the conventional process.


Autoregressive neural network; particle swarm optimization; singular value decomposition

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