A Software System for the Discovery of Situations Involving Drivers in Storms

  • Markus Stocker
  • Okko Kauhanen
  • Mikko Hiirsalmi
  • Janne Saarela
  • Pekka Rossi
  • Mauno Rönkkö
  • Harri Hytönen
  • Ville Kotovirta
  • Mikko Kolehmainen
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)

Abstract

We present an environmental software system that obtains, integrates, and reasons over situational knowledge about natural phenomena and human activity. We focus on storms and driver directions. Radar data for rainfall intensity and Google Directions are used to extract situational knowledge about storms and driver locations along directions, respectively. Situational knowledge about the environment and about human activity is integrated in order to infer situations in which drivers are potentially at higher risk. Awareness of such situations is of obvious interest. We present a prototype user interface that supports adding scheduled driver directions and the visualization of situations in space-time, in particular also those in which drivers are potentially at higher risk. We think that the system supports the claim that the concept of situation is useful for the modelling of information about the environment, including human activity, obtained in environmental monitoring systems. Furthermore, the presented work shows that situational knowledge, represented by heterogeneous systems that share the concept of situation, is relatively straightforward to integrate.

Keywords

Environmental knowledge systems situation theory ontology knowledge representation MMEA Wavellite 

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References

  1. 1.
    Mulligan, M., Wainwright, J.: Environmental Modelling: Finding Simplicity in Complexity. In: Modelling and Model Building, pp. 7–73. John Wiley & Sons, Ltd (2004)Google Scholar
  2. 2.
    Finkelstein, L.: Theory and Philosophy of Measurement. In: Sydenham, P.H. (ed.) Handbook of Measurement Science. Theoretical Fundamentals, vol. 1, pp. 1–30. John Wiley & Sons (1982)Google Scholar
  3. 3.
    Devlin, K.: Logic and Information. Cambridge University Press (1991)Google Scholar
  4. 4.
    Barwise, J., Perry, J.: Situations and Attitudes. The Journal of Philosophy 78(11), 668–691 (1981), http://www.jstor.org/stable/2026578 CrossRefGoogle Scholar
  5. 5.
    Stocker, M., Baranizadeh, E., Portin, H., Komppula, M., Rönkkö, M., Hamed, A., Virta-nen, A., Lehtinen, K., Laaksonen, A., Kolehmainen, M.: Representing situational knowl-edge acquired from sensor data for atmospheric phenomena. Environmental Modelling & Software 58, 27–47 (2014), http://www.sciencedirect.com/science/article/pii/S1364815214001108 CrossRefGoogle Scholar
  6. 6.
    Stocker, M., Rönkkö, M., Kolehmainen, M.: Situational knowledge representation for traffic observed by a pavement vibration sensor network. IEEE Transactions on Intelligent Transportation Systems 15(4), 1441–1450 (2014)CrossRefGoogle Scholar
  7. 7.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. Recommendation, W3C (January 2008), http://www.w3.org/TR/2008/REC-rdf-sparql-query-20080115/
  8. 8.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press, Menlo Park (1996)Google Scholar
  9. 9.
    Dixon, M., Wiener, G.: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting–A Radar-based Methodology. Journal of Atmospheric and Oceanic Technology 10(6), 785–797 (1993)CrossRefGoogle Scholar
  10. 10.
    Kokar, M.M., Matheus, C.J., Baclawski, K.: Ontology-based situation awareness. Inf. Fusion 10(1), 83–98 (2009)CrossRefGoogle Scholar
  11. 11.
    Manola, F., Miller, E., McBride, B.: RDF Primer. W3C Recommendation, W3C (February 2004)Google Scholar
  12. 12.
    W3C OWL Working Group: OWL 2 Web Ontology Language Document Overview (Second Edition). Recommendation, W3C (December 2012), http://www.w3.org/TR/2012/REC-owl2-overview-20121211/
  13. 13.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 37(1), 32–64 (1995)CrossRefGoogle Scholar
  14. 14.
    Szczerba, R., Galkowski, P., Glicktein, I., Ternullo, N.: Robust algorithm for real-time route planning. IEEE Transactions on Aerospace and Electronic Systems 36(3), 869–878 (2000)CrossRefGoogle Scholar
  15. 15.
    Balke, W.T., Kiessling, W., Unbehend, C.: A situation-aware mobile traffic information system. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences (January 2003)Google Scholar
  16. 16.
    Stanton, N.A., Stewart, R., Harris, D., Houghton, R.J., Baber, C., McMaster, R., Salmon, P., Hoyle, G., Walker, G., Young, M.S., Linsell, M., Dymott, R., Green, D.: Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology. Ergonomics 49(12-13), 1288–1311 (2006), http://dx.doi.org/10.1080/00140130600612762 pMID: 17008257

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Markus Stocker
    • 1
  • Okko Kauhanen
    • 1
  • Mikko Hiirsalmi
    • 2
  • Janne Saarela
    • 3
  • Pekka Rossi
    • 4
  • Mauno Rönkkö
    • 1
  • Harri Hytönen
    • 5
  • Ville Kotovirta
    • 2
  • Mikko Kolehmainen
    • 1
  1. 1.Department of Environmental ScienceUniversity of Eastern FinlandKuopioFinland
  2. 2.VTT Technical Research Center of FinlandEspooFinland
  3. 3.Profium OyHelsinkiFinland
  4. 4.Finnish Meteorological InstituteHelsinkiFinland
  5. 5.Vaisala OyjVantaaFinland

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