Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/55385
Título: The bus bunching problem: empirical findings from spatial analytics
Autores/as: Iliopoulou, Christina
Milioti, Christina
Vlahogianni, Eleni
Kepaptsoglou, Konstantinos
Sánchez-Medina, Javier J. 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Bus bunching
Spatial autocorrelation
Spatio-temporal clustering
Fecha de publicación: 2018
Conferencia: 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) 
Resumen: Service regularity is one of the most significant performance indicators for public transport routes, typically measured through headway adherence. When headway deviations become too large and corresponding headways very small, bus bunching typically occurs. In these cases, passengers experience larger waiting times and overcrowding and an overall poor level of service. This paper aims to gain insight on frequent patterns of bus bunching using spatial analytics. Local and global spatial autocorrelation tests are performed on real world Automatic Vehicle Location (AVL) data to investigate spatial structures in the data. The spatio-temporal variations of bus bunching patterns throughout the day are further modeled using the ST-DBSCAN algorithm. Results show that the last few stops of each route exhibit statistically significant spatial autocorrelation with respect to the frequency of bunching, while the duration of bunching events is longer for route segments located in the central business district. Spatio-temporal clustering indicates that bunching is observed at a higher number of stops during peak traffic periods.
URI: http://hdl.handle.net/10553/55385
ISBN: 978-1-7281-0323-5
ISSN: 2153-0009
DOI: 10.1109/ITSC.2018.8569760
Fuente: 2018 21st International Conference on Intelligent Transportation Systems (ITSC) Maui, Hawaii, USA, November 4-7, 2018, p. 871-876
URL: https://api.elsevier.com/content/abstract/scopus_id/85060436667
Colección:Actas de congresos
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