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Title: Using data mining to analyze dwell time and nonstop running time in road-based mass transit systems
Authors: Cristóbal, Teresa
Padrón Morales, Gabino 
Quesada Arencibia, Francisco Alexis 
Alayón Hernández, Francisco Javier 
De Blasio , Gabriele Salvatore 
García Rodríguez, Carmelo Rubén 
UNESCO Clasification: 3327 Tecnología de los sistemas de transporte
120304 Inteligencia artificial
Keywords: Road-based mass transit systems
Travel time
Intelligent transport systems
Big data
Data mining, et al
Issue Date: 2018
Journal: Proceedings (MDPI) 
Conference: 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence - UCAmI 2018 
Abstract: Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.
ISSN: 2504-3900
DOI: 10.3390/proceedings2191217
Source: Proceedings (MDPI) [ISSN 2504-3900], v. 2 (19), 1217
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