| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000383475 |
| ISSN: | 1870-9044 |
| Autores: | Barba, Lida1 Rodríguez, Nibaldo2 |
| Instituciones: | 1Universidad Nacional de Chimborazo, Escuela de Ingeniería de Computación, Riobamba, Chimborazo. Ecuador 2Pontificia Universidad Católica de Valparaíso, Valparaíso. Chile |
| Año: | 2015 |
| Periodo: | Ene-Jun |
| Número: | 51 |
| Paginación: | 33-38 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | 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 irst 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 |
| Disciplinas: | Ciencias de la computación, Ingeniería |
| Palabras clave: | Redes, Ingeniería de transportes, Predicción, Accidentes de tránsito, Descomposición singular, Optimización por enjambre de partículas |
| Keyword: | Computer science, Engineering, Networks, Transportation engineering, Forecasting, Singular value decomposition, Particle swarm optimization, Traffic accidents |
| Texto completo: | Texto completo (Ver HTML) |
