Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/40196
Title: | Methodology for analyzing the travel time variability in public road transport | Authors: | Cristóbal Betancor, Teresa Padrón, Gabino Quesada-Arencibia, Alexis Alayón, Francisco García Rodríguez, Carmelo Rubén |
UNESCO Clasification: | 3327 Tecnología de los sistemas de transporte 120304 Inteligencia artificial |
Keywords: | Automatic vehicle location Automatic. public transport planning GPS data Intelligent transport systems |
Issue Date: | 2017 | Publisher: | Springer | Journal: | Lecture Notes in Computer Science | Conference: | 11th International Conference on Ubiquitous Computing and Ambient Intelligence, (UCAmI 2017) | Abstract: | The quality of the time travel prediction is a key factor in the transport of people and goods. This prediction is used in different facets related to management and planning of the transport activity, having special influence in the service quality in public transport. In this paper a methodology to analyse the factors which affect to travel time prediction in routes of road public transport is presented. This methodology uses vehicles GPS data to identify the causes of the travel time variability, georeferencing these causes. The infrastructure elements required, data used and the processing techniques are explained. The methodology was applied to analyse the travel time of a line of a public transport company, presenting the results of this test. | URI: | http://hdl.handle.net/10553/40196 | ISBN: | 978-3-319-67584-8 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-319-67585-5_5 | Source: | Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science, v. 10586 LNCS, p. 44-49 |
Appears in Collections: | Capítulo de libro |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.