A new Spatio-Temporal neural network approach for traffic accident forecasting

Medrano López, Rodrigo de. (2019). A new Spatio-Temporal neural network approach for traffic accident forecasting Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
DeMedrano_Lopez_Rodrigo_TFM.pdf DeMedrano_Lopez_Rodrigo_TFM.pdf application/pdf 1.16MB

Título A new Spatio-Temporal neural network approach for traffic accident forecasting
Autor(es) Medrano López, Rodrigo de
Abstract Traffic accidents forecasting represents a major priority for traffic governmental organisms around the world to ensure a decrease in life, property and economic losses. The increasing amounts of traffic accident data have been used to train machine learning predictors, although this is a challenging task due to the relative rareness of accidents, inter-dependencies of traffic accidents both in time and space and high dependency on human behavior. Recently, deep learning techniques have shown significant prediction improvements over traditional models, but some difficulties and open questions remain around their applicability, accuracy and ability to provide practical information. This paper proposes a new spatio-temporal deep learning framework based on a latent model for simultaneously predicting the number of traffic accidents in each neighborhood in Madrid, Spain, over varying training and prediction time horizons.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
Director/Tutor Aznarte Mellado, José Luis
Fecha 2019-09-26
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Rmedrano
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Rmedrano
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 545 Visitas, 441 Descargas  -  Estadísticas en detalle
Creado: Tue, 13 Oct 2020, 23:10:14 CET