Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2015
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| _version_ | 1876464510939693056 |
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| author | Lida Barba |
| author_facet | Lida Barba |
| contents | Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO Lida Barba Nibaldo Rodríguez Computación particle swarm optimization singular value decomposition Autoregressive neural network In this paper, we propose a strategy to improvethe forecasting of traffic accidents in Concepci ́on, 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 thesecond stage, the Singular Value Decomposition (SVD) techniqueis applied. SVD extracts the singular values and the singularvectors, which are used to obtain the components of low andhigh frequency. At the third stage, the estimation is implementedwith an Autoregressive Neural Network (ANN) based on ParticleSwarm Optimization (PSO). The final stage is recomposition,where the forecasted value is obtained. The results are comparedwith the values given by the conventional forecasting process. Ourstrategy shows high accuracy and is superior to the conventionalprocess. 2015 artículo científico 1870-9044 https://www.redalyc.org/articulo.oa?id=402641203006 en http://www.redalyc.org/revista.oa?id=4026 Polibits application/pdf Instituto Politécnico Nacional Polibits (México) Vol.51 |
| format | Artículo científico |
| id | redalyc_402641203006 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO Lida Barba Computación particle swarm optimization singular value decomposition Autoregressive neural network Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO Lida Barba Nibaldo Rodríguez Computación particle swarm optimization singular value decomposition Autoregressive neural network In this paper, we propose a strategy to improvethe forecasting of traffic accidents in Concepci ́on, 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 thesecond stage, the Singular Value Decomposition (SVD) techniqueis applied. SVD extracts the singular values and the singularvectors, which are used to obtain the components of low andhigh frequency. At the third stage, the estimation is implementedwith an Autoregressive Neural Network (ANN) based on ParticleSwarm Optimization (PSO). The final stage is recomposition,where the forecasted value is obtained. The results are comparedwith the values given by the conventional forecasting process. Ourstrategy shows high accuracy and is superior to the conventionalprocess. 2015 artículo científico 1870-9044 https://www.redalyc.org/articulo.oa?id=402641203006 en http://www.redalyc.org/revista.oa?id=4026 Polibits application/pdf Instituto Politécnico Nacional Polibits (México) Vol.51 |
| title | Traffic Accidents Forecasting using Singular Value Decomposition and an Autoregressive Neural Network Based on PSO |
| topic | Computación particle swarm optimization singular value decomposition Autoregressive neural network |
| url | https://www.redalyc.org/articulo.oa?id=402641203006 |