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

J. Nin; David Carrera; Daniel Villatoro

doi:10.1007/978-3-319-04178-0_6

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

J. Nin, David Carrera, Daniel Villatoro

Research output: Book chapter › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers
Editors	Jordi Nin, Daniel Villatoro
Publisher	Springer Verlag
Pages	59-70
Number of pages	12
ISBN (Electronic)	9783319041773
DOIs	https://doi.org/10.1007/978-3-319-04178-0_6
Publication status	Published - 2014
Externally published	Yes
Event	2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013 - Barcelona, Spain Duration: 19 Sept 2013 → 19 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8313
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013
Country/Territory	Spain
City	Barcelona
Period	19/09/13 → 19/09/13

Keywords

Cloud computing
Geospatial clustering
High performance computing
Metric spaces
OSA distance
Smart cities

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-04178-0_6

Cite this

Nin, J., Carrera, D., & Villatoro, D. (2014). On the use of social trajectory-based clustering methods for public transport optimization. In J. Nin, & D. Villatoro (Eds.), Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers (pp. 59-70). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8313). Springer Verlag. https://doi.org/10.1007/978-3-319-04178-0_6

Nin, J. ; Carrera, David ; Villatoro, Daniel. / On the use of social trajectory-based clustering methods for public transport optimization. Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers. editor / Jordi Nin ; Daniel Villatoro. Springer Verlag, 2014. pp. 59-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "On the use of social trajectory-based clustering methods for public transport optimization",

abstract = "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.",

keywords = "Cloud computing, Geospatial clustering, High performance computing, Metric spaces, OSA distance, Smart cities",

author = "J. Nin and David Carrera and Daniel Villatoro",

note = "Funding Information: This work is partially supported by the Ministry of Science and Technology of Spain under contract TIN2012-34557 and by the BSC-CNS Severo Ochoa program (SEV-2011-00067) and with the support of ACC1{\'O}, the Catalan Agency to promote applied research and innovation; and by the Spanish Centre for Development of Industrial Technology under the INNPRONTA program, project IPT-20111006, “CIUDAD2020”. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014; 2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013 ; Conference date: 19-09-2013 Through 19-09-2013",

year = "2014",

doi = "10.1007/978-3-319-04178-0_6",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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address = "Germany",

}

Nin, J, Carrera, D & Villatoro, D 2014, On the use of social trajectory-based clustering methods for public transport optimization. in J Nin & D Villatoro (eds), Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8313, Springer Verlag, pp. 59-70, 2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013, Barcelona, Spain, 19/09/13. https://doi.org/10.1007/978-3-319-04178-0_6

On the use of social trajectory-based clustering methods for public transport optimization. / Nin, J.; Carrera, David; Villatoro, Daniel.
Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers. ed. / Jordi Nin; Daniel Villatoro. Springer Verlag, 2014. p. 59-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8313).

Research output: Book chapter › Conference contribution › peer-review

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AU - Carrera, David

AU - Villatoro, Daniel

N1 - Funding Information: This work is partially supported by the Ministry of Science and Technology of Spain under contract TIN2012-34557 and by the BSC-CNS Severo Ochoa program (SEV-2011-00067) and with the support of ACC1Ó, the Catalan Agency to promote applied research and innovation; and by the Spanish Centre for Development of Industrial Technology under the INNPRONTA program, project IPT-20111006, “CIUDAD2020”. Publisher Copyright: © Springer International Publishing Switzerland 2014

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Cloud computing

KW - Geospatial clustering

KW - High performance computing

KW - Metric spaces

KW - OSA distance

KW - Smart cities

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers

A2 - Nin, Jordi

A2 - Villatoro, Daniel

PB - Springer Verlag

T2 - 2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013

Y2 - 19 September 2013 through 19 September 2013

ER -

Nin J, Carrera D, Villatoro D. On the use of social trajectory-based clustering methods for public transport optimization. In Nin J, Villatoro D, editors, Citizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers. Springer Verlag. 2014. p. 59-70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-04178-0_6

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

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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