Understanding personal mobility patterns for proactive recommendations

Ruben M. Costa, Paulo Alves Figueiras, Pedro Oliveira, Ricardo Jardim-Goncalves

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


This paper proposes an innovative methodology for extracting and learning personal mobility patterns. The objective is to award daily commuters in a city with personalized and proactive recommendations, related with their mobility habits on a daily basis. In currently approaches, users have to explicitly provide their routes (origin, destination and date/time) to a routing engine in order to be notified about traffic events. The proposed approach goes beyond and learns daily mobility habits from the users, without the need to provide any information. The work presented here, is currently being addressed under the EU OPTIMUM project. Results achieved establish the basis for the formalization of the OPTIMUM domain knowledge on personal mobility patterns.

Original languageEnglish
Title of host publicationOn the Move to Meaningful Internet Systems: OTM 2015 Workshops - Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, EI2N, FBM, INBAST, ISDE, META4eS, and MSC 2015, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)978-3-319-26138-6;
Publication statusPublished - 2015
EventInternational Workshops on the Move to Meaningful Internet Systems, OTM 2015 - Rhodes, Greece
Duration: 26 Oct 201530 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Workshops on the Move to Meaningful Internet Systems, OTM 2015


  • Data acquisition
  • Intelligent transport systems
  • Machine learning
  • Mobility patterns


Dive into the research topics of 'Understanding personal mobility patterns for proactive recommendations'. Together they form a unique fingerprint.

Cite this