On the use of social trajectory-based clustering methods for public transport optimization

Jordi Nin, David Carrera, Daniel Villatoro

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Public transport optimisation is becoming everyday a more difficult and challenging task, because of the increasing number of transportation options as well as the increase of users. Many research contributions about this issue have been recently published under the umbrella of the smart cities research. In this work, we sketch a possible framework to optimize the tourist bus in the city of Barcelona. Our framework will extract information from Twitter and other web services, such as Foursquare to infer not only the most visited places in Barcelona, but also the trajectories and routes that tourist follow. After that, instead of using complex geospatial or trajectory clustering methods, we propose to use simpler clustering techniques as k-means or DBScan but using a real sequence of symbols as a distance measure to incorporate in theclustering process the trajectory information.

Idioma originalInglés
Título de la publicación alojadaCitizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers
EditoresJordi Nin, Daniel Villatoro
EditorialSpringer Verlag
Páginas59-70
Número de páginas12
ISBN (versión digital)9783319041773
DOI
EstadoPublicada - 2014
Publicado de forma externa
Evento2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013 - Barcelona, Espana
Duración: 19 sept 201319 sept 2013

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8313
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013
País/TerritorioEspana
CiudadBarcelona
Período19/09/1319/09/13

Huella

Profundice en los temas de investigación de 'On the use of social trajectory-based clustering methods for public transport optimization'. En conjunto forman una huella única.

Citar esto