Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44275
Título: A simple classification approach to traffic flow state estimation
Autores/as: del Pino Saavedra Hernández, Aitor
Sánchez Medina, Javier J. 
Moraine-Matias, Luis
Clasificación UNESCO: 120304 Inteligencia artificial
332702 Análisis del trafico
Palabras clave: Decision making
Environmental impact
Traffic control
Highway planning
Street traffic control, et al.
Fecha de publicación: 2018
Editor/a: Springer 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 16th International Conference on Computer Aided Systems Theory, (EUROCAST 2017) 
Resumen: One of the most important elements in the mobility of the developed cities is the road traffic management. The mobility determines the quality of citizens’ living conditions because of many reasons, security, efficiency, and the environmental impact. Focusing on security, according to World Health Organization (WHO), every year two millions of people die as a result of traffic accidents. Moreover between twenty and fifty millions of people suffer non-fatal injuries and a proportion of these people suffer from a disability. These injuries affect both the family economy and the country. For this reason, amongst others, it is required to equip the mobility managers with the proper tools to get a precise idea about the current situation and estimate future state. These tools facilitate the decision-making and the development of mobility.
URI: http://hdl.handle.net/10553/44275
ISBN: 978-3-319-74727-9
ISSN: 0302-9743
DOI: 10.1007/978-3-319-74727-9_52
Fuente: Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science, v. 10672 LNCS, p. 435-439
Colección:Capítulo de libro
Vista completa

Visitas

144
actualizado el 24-ago-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.