An Ontology-Based Traffic Accident Risk Mapping Framework

  • Jing Wang
  • Xin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)

Abstract

Road traffic accidents are a social and public challenge. Various spatial concentration detection methods have been proposed to discover the concentration patterns of traffic accidents. However, current methods treat each traffic accident location as a point without consideration of the severity level, and the final traffic accident risk map for the whole study area ignores the users’ requirements. In this paper, we propose an ontology-based traffic accident risk mapping framework. In the framework, the ontology represents the domain knowledge related to the traffic accidents and supports the data retrieval based on users’ requirements. A new spatial clustering method that takes into account the numbers and severity levels of accidents is proposed for risk mapping. To demonstrate the framework, a system prototype has been implemented. A case study in the city of Calgary is also discussed.

Keywords

Spatial clustering GIS Ontology Traffic accident Road safety 

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References

  1. 1.
    Anderson, T.K.: Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention 41(3), 359–364 (2009)CrossRefGoogle Scholar
  2. 2.
    Black, W.R., Thomas, I.: Accidents on Belgiums motorways: a network autocorrelation analysis. Journal of Transport Geography 6 (March 23-31, 1998)Google Scholar
  3. 3.
    Borruso, G.: Network density estimation: analysis of point patterns over a network. In: Osvaldo, G., Marina, L.G., Vipin, K., Antonio, L., Heow, P.L., Youngsong, M., David, T., Chih Jeng, K.T. (eds.) ICCSA 2005. LNCS, vol. 3482, pp. 126–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Boots, B.N., Getis, A.: Point Pattern Analysis, Sage Newbury Park, CA (1988)Google Scholar
  5. 5.
  6. 6.
    Doherty, S.T., Andrey, J.C., MacGregor, C.: The situational risks of young drivers: The influence of passengers, time of day and day of week on accident rates. Accident Analysis & Prevention 30(1), 45–52 (1998)CrossRefGoogle Scholar
  7. 7.
    Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. International Journal of Geographical Information Systems 5(2), 161–174 (1991)CrossRefGoogle Scholar
  8. 8.
    Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)Google Scholar
  9. 9.
    Flahaut, B., Mouchart, M., Martin, E.S., Thomas, I.: The local spatial autocorrelation and the kernel method for identifying black zones a comparative approach. Accident Analysis & Prevention 35, 991–1004 (2003)CrossRefGoogle Scholar
  10. 10.
    Getis, A.: A history of the concept of spatial autocorrelation: a geographer’s perspective. Geographical Analysis 40, 297–309 (2008)CrossRefGoogle Scholar
  11. 11.
    Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  12. 12.
    Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological engineering: with examples from the areas of knowledge management, e-commerce and the Semantic Web. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Han, J., Kamber, M., Tung, A.K.H.: Spatial Clustering Methods in Data Mining: A Survey. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery. Taylor and Francis, London (2001)Google Scholar
  14. 14.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)MATHGoogle Scholar
  15. 15.
    Hwang, J.: Ontology-based spatial clustering method: case study of traffic accidents. Student Paper Sessions, UCGIS Summer Assembly (2003)Google Scholar
  16. 16.
    Hwang, S.: Using Formal Ontology for Integrated Spatial Data Mining. In: Computational Science and Its Applications – ICCSA 2004, pp. 1026–1035 (2004)Google Scholar
  17. 17.
    Maedche, A., Zacharias, V.: Clustering ontology-based metadata in the semantic web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 348–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Okabe, A., Yamada, I.: The K-function method on a network and its computational implementation. Geographical Analysis 33(3), 271–290 (2001)CrossRefGoogle Scholar
  19. 19.
    Okabe, A., Satoh, T., Sugihara, K.: A kernel density estimation method for networks, its computational method and a GIS-based tool. International Journal of Geographical Information Science 23(1), 7–32 (2009)CrossRefGoogle Scholar
  20. 20.
    Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E., Mathers, C. (eds.): World Report on Road Traffic Injury Prevention. World Health Organization, Geneva (2004)Google Scholar
  21. 21.
    Peuquet, D.J.: Representations of Space and Time. Guilford, New York (2002)Google Scholar
  22. 22.
    PIARC, Road Safety Manual, World Roads Association Cedex (2003)Google Scholar
  23. 23.
  24. 24.
    RememberRoadCrashVictims.ca (2009), http://www.RememberRoadCrashVictims.ca
  25. 25.
    Rifaat, S.M., Tay, R.: Effect of Street Pattern on Road Safety: Are Policy Recommendations Sensitive to Different Aggregations of Crashes by Severity? Transportation Research Record: Journal of the Transportation Research Board, 58–65 (2010)Google Scholar
  26. 26.
    Shino, S.: Analysis of a distribution of point events using the network-based quadrat method. Geographical Analysis 40, 380–400 (2008)CrossRefGoogle Scholar
  27. 27.
    Smith, M.K., Welty, C., McGuinness, D.L.: OWL Web Ontology Language Guide. W3C (2004), http://www.w3.org/TR/owl-guide/
  28. 28.
    Steenberghen, T., Aerts, K., Thomas, I.: Spatial clustering of events on a network. Journal of Transport Geography 18, 411–418 (2010)CrossRefGoogle Scholar
  29. 29.
    Steenberghen, T., Dufays, T., Thomas, I., Flahaut, B.: Intra-urban location and clustering of road accidents using GIS: a Belgian example. International Journal of Geographical Information Science 18(2), 169–181 (2004)CrossRefGoogle Scholar
  30. 30.
    Stefanakis, E.: NET-DBSCAN: clustering the nodes of a dynamic linear network. International Journal of Geographical Information Science 21(4), 427–442 (2007)CrossRefGoogle Scholar
  31. 31.
    Wang, X., Gu, W., Ziébelin, D., Hamilton, H.: An Ontology-Based Framework for Geospatial Clustering. International Journal of Geographical Information Science 24(1), 1601–1630 (2010)CrossRefGoogle Scholar
  32. 32.
    Wang, X., Hamilton, H.J.: Towards An Ontology-Based Spatial Clustering Framework. In: Proceedings of the Eighteenth Canadian Artificial Intelligence Conference (AI 2005), Victoria, Canada, pp. 205–216 (2005)Google Scholar
  33. 33.
    Xie, Z., Yan, J.: Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems 32, 396–406 (2008)CrossRefGoogle Scholar
  34. 34.
    Yamada, I., Thill, J.-C.: Comparison of planar and network K-functions in traffic accident analysis. Journal of Transport Geography 12, 149–158 (2004)CrossRefGoogle Scholar
  35. 35.
    Yue, D., Wang, S., Zhao, A.: Traffic Accidents Knowledge Management Based on Ontology. In: Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 07, pp. 447–449. IEEE Computer Society, Los Alamitos (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Wang
    • 1
  • Xin Wang
    • 1
  1. 1.Department of Geomatics EngineeringUniversity of CalgaryCalgaryCanada

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