Skip to main content

Predicting Individual Trip Destinations with Artificial Potential Fields

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10268)

Abstract

This paper presents a method to model the intended destination of a subject in real time, based on a trace of position information and prior knowledge of possible destinations. In contrast to most work in this field, it does so without the need for prior analysis of habitual travel patterns. The method models the certainty of each POI by means of a virtual charge, resulting in an artificial potential field that reflects the current estimate of the subject’s intentions. The virtual charges are updated as new information about the subject’s position arrives. We experimentally compare a number of update rules with various parameter settings, showing that it is important to take the distance to a potential destination into account when updating the charge.

Keywords

  • Human behavior
  • Intention analysis
  • Destination prediction
  • GPS
  • Trajectory database

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-59513-9_12
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-59513-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

References

  1. Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput. 7(5), 275–286 (2003)

    CrossRef  Google Scholar 

  2. Nicholson, A.J., Noble, B.D.: BreadCrumbs: forecasting mobile connectivity. In: Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, vol. 2, pp. 46–57 (2008)

    Google Scholar 

  3. Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T.: NextPlace: a spatio-temporal prediction framework for pervasive systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 152–169. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21726-5_10

    CrossRef  Google Scholar 

  4. Do, T.M.T., Gatica-Perez, D.: Where and what: using smartphones to predict next locations and applications in daily life. Pervasive Mob. Comput. 12, 79–91 (2014)

    CrossRef  Google Scholar 

  5. Sadilek, A., Krumm, J., Out, F.: Predicting long-term human mobility. In: 26th AAAI Conference on Artificial Intelligence, pp. 814–820 (2012)

    Google Scholar 

  6. Ziebart, B.D., Maas, A.L., Dey, A.K., Bagnell, J.A.: Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior, pp. 322–331 (2008)

    Google Scholar 

  7. Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Building personal maps from GPS data. In: International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Modeling Others from Observations (2005)

    Google Scholar 

  8. Fallis, A.: Real-time travel path prediction using GPS-enabled mobile phones. J. Chem. Inf. Model. 53(9), 1689–1699 (2013)

    Google Scholar 

  9. Lorenzo, G.D., Phithakkitnukoon, S., Horanont, T., Lorenzo, G.D., Map, A.-A.: Identifying human daily activity pattern using mobile phone data. In: Proceedings of the First International Conference on Human Behavior Understanding, pp. 14–25 (2010)

    Google Scholar 

  10. Ying, J.J.-C., Lee, W.-C., Weng, T.-C., Tseng, V.S.: Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS 2011, p. 34 (2011)

    Google Scholar 

  11. Zheng, K., Zheng, Y., Yuan, N.J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories. IEEE Trans. Knowl. Data Eng. 26(8), 1974–1988 (2014)

    CrossRef  Google Scholar 

  12. Hwang, Y.K., Ahuja, N.: A potential field approach to path planning. IEEE Trans. Robot. Autom. 8(1), 23–32 (1992)

    CrossRef  Google Scholar 

  13. Howard, A., Mataric, M.J., Sukhatme, G.S.: Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds.) Distributed Autonomous Robotic Systems, vol. 5, pp. 299–308. Springer, Tokyo (2002)

    Google Scholar 

  14. Parunak, H., Purcell, L., Six, F., Station, N., O’Connell, M.: Digital pheromones for autonomous coordination of swarming UAV’s. In: 1st UAV Conference, Infotech@Aerospace Conferences (2002)

    Google Scholar 

  15. Mottaghi, R., Vaughan, R.: An integrated particle filter and potential field method applied to cooperative multi-robot target tracking. Autonom. Robots 23(1), 19–35 (2007)

    CrossRef  Google Scholar 

  16. Helble, H., Cameron, S.: 3-D path planning and target trajectory prediction for the Oxford aerial tracking system. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1042–1048 (2007)

    Google Scholar 

  17. de Jong, S., Klein, A., Smelik, R., van Wermeskerken, F.: Integrating run-time incidents in a large-scale simulated urban environment. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 1401–1402 (2016)

    Google Scholar 

  18. Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing - UbiComp 2008, vol. 49, p. 312 (2008)

    Google Scholar 

  19. Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web - WWW 2009, vol. 49, p. 791 (2009)

    Google Scholar 

  20. Zheng, Y., Xie, X., Ma, W.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–40 (2010)

    Google Scholar 

Download references

Acknowledgement

We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster. The research for this paper was financially supported by the Netherlands Organisation for Applied Scientific Research (TNO).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alessandro Zonta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zonta, A., Smit, S.K., Haasdijk, E. (2017). Predicting Individual Trip Destinations with Artificial Potential Fields. In: Alba, E., Chicano, F., Luque, G. (eds) Smart Cities. Smart-CT 2017. Lecture Notes in Computer Science(), vol 10268. Springer, Cham. https://doi.org/10.1007/978-3-319-59513-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59513-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59512-2

  • Online ISBN: 978-3-319-59513-9

  • eBook Packages: Computer ScienceComputer Science (R0)