Where do you Roll Today? Trajectory Prediction by SpaceRank and Physics Models

  • Stefano De Sabbata
  • Stefano Mizzaro
  • Luca Vassena
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Pre-destination, the prediction of a user’s future destination, is recently gaining interest and importance in location-aware, ubiquitous, and mobile computing. An increasing amount of data related to position of people is becoming available because people usually take their mobile devices (phones, smart-phones, PDAs, etc.) with them. We propose to mine these data to derive the importance of the single locations in an area of interest, given by either a single user or a community. Then we use the importance of locations as basis for our approach to pre-destination, where well-known physics models (namely gravitation and electrical force) are exploited to estimate users trajectories and future destinations.


location-awareness location importance physics models trajectory destination prevision 


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  1. Ashbrook D, Starner T (2003) Using GPS to learn signifi cant locations and predict movement across multiple users, Personal and Ubiquitous Computing7(5), 275–286.CrossRefGoogle Scholar
  2. Bierlairea M, Frejinger E (2008) Route choice modeling with network-free data, Transportation Research Part C: Emerging Technologies16, 187–198.CrossRefGoogle Scholar
  3. Chan J, Zhou S, Seneviratne A (1998) A QoS adaptive mobility prediction scheme for wireless networks, inGlobal Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration. IEEE, Vol. 3, pp. 1414–1419.Google Scholar
  4. De Sabbata S, Mizzaro S, Vassena L (2008) SpaceRank: Using PageRank to estimate location importance, inProceedings of ECAI ’08 Workshop on Mining Social Data (MSoDa ’08), pp. 1–5, University of Patras, Greece.Google Scholar
  5. Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems, Personal and Ubiquitous Computing10(4), 255–268.CrossRefGoogle Scholar
  6. Gonzalez MC, Hidalgo CA, Barabasi AL (2008) Understanding individual human mobility patterns, Nature435(7196), 799–782.Google Scholar
  7. Hazas M, Scott J, Krumm J (2004) Location-aware computing comes of age, Computer 37(2), 95–97.CrossRefGoogle Scholar
  8. Krumm J, Horvitz E (2006) Predestination: Inferring destinations from partial trajectories, in Lecture Notes in Computer Science, Vol. 4206, Springer, pp. 243–260.CrossRefGoogle Scholar
  9. Mountain DM (2005) Exploring mobile trajectories: An investigation of individual spatial behaviour and geographic fi lters for information retrieval, PhD thesis, City University.Google Scholar
  10. Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: Bringing order to the web, Technical report, Stanford Digital Library Technologies Project.Google Scholar
  11. Taylor MAP, Woolley JE, Zito R (2000) Integration of the global positioning system and geographical information systems for traffi c congestion studies, Transportation Research Part C: Emerging Technologies8, 257–285.CrossRefGoogle Scholar
  12. Zaidi ZR, Mark BL (2005) Real-time mobility tracking algorithms for cellular networks based on kalman fi ltering, IEEE Transactions on Mobile Computing4(2), 195–208.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefano De Sabbata
    • 1
  • Stefano Mizzaro
    • 1
  • Luca Vassena
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of UdineItaly

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