Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43560
Título: Arrival time estimation system based on massive positioning data of public transport vehicles
Autores/as: Padrón, Gabino 
Alayón, Francisco 
Cristóbal, Teresa
Quesada-Arencibia, Alexis 
García, Carmelo R. 
Clasificación UNESCO: 120304 Inteligencia artificial
3327 Tecnología de los sistemas de transporte
Palabras clave: Intelligent transport systems
Float car data
Pattern recognition
Artificial intelligence
Bus transportation, et al.
Fecha de publicación: 2016
Editor/a: Springer 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016) 
Resumen: Nowadays the public transport systems play a main role in the advanced societies. How to evaluate the quality of the public transport is a critic task of the transport regulatory agencies. One indicator used to measure this quality is the fulfilment of the scheduled operation by the transport operators, specially scheduled arrival times and frequencies at stops. In this paper, an automatic system to estimate arrival times in the context of road public transport is proposed. The system works autonomously, acquiring massive vehicle position readings, registering and processing them automatically in order to estimate arrival times. This autonomous behaviour is achieved using pattern recognition and statistical techniques. To illustrate the application of the system, the estimation of arrival times at a bus stop is presented.
URI: http://hdl.handle.net/10553/43560
ISBN: 978-3-319-48798-4
ISSN: 0302-9743
DOI: 10.1007/978-3-319-48799-1_44
Fuente: Ubiquitous Computing and Ambient Intelligence. IWAAL 2016, AmIHEALTH 2016, UCAmI 2016. Lecture Notes in Computer Science, v. 10070, p. 395-406
Colección:Capítulo de libro
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