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

Jordi Nin, David Carrera, Daniel Villatoro

Research output: Book chapterConference contributionpeer-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 languageEnglish
Title of host publicationCitizen in Sensor Networks - 2nd International Workshop, CitiSens 2013, Revised Selected Papers
EditorsJordi Nin, Daniel Villatoro
PublisherSpringer Verlag
Pages59-70
Number of pages12
ISBN (Electronic)9783319041773
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013 - Barcelona, Spain
Duration: 19 Sept 201319 Sept 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8313
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Citizen in Sensor Networks, CitiSens 2013
Country/TerritorySpain
CityBarcelona
Period19/09/1319/09/13

Keywords

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

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