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

Vista sencilla de ítem
dc.contributor.authorMedrano López, Rodrigo de
dc.date.accessioned2024-05-20T12:35:15Z
dc.date.available2024-05-20T12:35:15Z
dc.date.issued2019-09-26
dc.description.abstractTraffic 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.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14564
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleA new Spatio-Temporal neural network approach for traffic accident forecastinges
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
DeMedrano_Lopez_Rodrigo_TFM.pdf
Tamaño:
1.17 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Trabajos de fin de máster (TFM)