Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

  • Freddy Lécué
  • Robert Tucker
  • Veli Bicer
  • Pierpaolo Tommasi
  • Simone Tallevi-Diotallevi
  • Marco Sbodio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semantic Web technologies. In particular we present a system which integrates numerous sensors (exposing heterogenous, exogenous and raw data streams such as weather information, road works, city events or incidents) to improve accuracy and consistency of traffic congestion prediction. Our prototype of semantics-aware prediction, being used and experimented currently by traffic controllers in Dublin City Ireland, works efficiently with real, live and heterogeneous stream data. The experiments have shown accurate and consistent prediction of road traffic conditions, main benefits of the semantic encoding.




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  1. 1.
    Schrank, D., Eisele, B.: 2012 urban mobility report (2012),
  2. 2.
    Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Physical Review E 51, 1035–1042 (1995)CrossRefGoogle Scholar
  3. 3.
    Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)Google Scholar
  4. 4.
    Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD, pp. 226–235 (2003)Google Scholar
  5. 5.
    Papadimitriou, S., Sun, J., Faloutsos, C.: Streaming pattern discovery in multiple time-series. In: VLDB, pp. 697–708 (2005)Google Scholar
  6. 6.
    Schrader, C.C., Kornhauser, A.L., Friese, L.M.: Using historical information in forecasting travel times. Transportation Research Board 51, 1035–1042 (2004)Google Scholar
  7. 7.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies 19(4), 606–616 (2011)CrossRefGoogle Scholar
  8. 8.
    Cairns, S., Hass-Klau, C., Goodwin, P.: Traffic impact of highway capacity reductions: Assessment of the evidence. Landor Publishing (1998)Google Scholar
  9. 9.
    Lécué, F., Pan, J.Z.: Predicting knowledge in an ontology stream. In: IJCAI (2013)Google Scholar
  10. 10.
    Lécué, F., Schumann, A., Sbodio, M.L.: Applying semantic web technologies for diagnosing road traffic congestions. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 114–130. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Han, L., Finin, T.W., Parr, C.S., Sachs, J., Joshi, A.: RDF123: From spreadsheets to RDF. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)Google Scholar
  13. 13.
    Baader, F., Lutz, C., Suntisrivaraporn, B.: CEL — A polynomial-time reasoner for life science ontologies. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 287–291. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Baader, F., Brandt, S., Lutz, C.: Pushing the el envelope. In: IJCAI, pp. 364–369 (2005)Google Scholar
  15. 15.
    Ren, Y., Pan, J.Z.: Optimising ontology stream reasoning with truth maintenance system. In: CIKM, pp. 831–836 (2011)Google Scholar
  16. 16.
    Ma, Y., Tran, T., Bicer, V.: Typifier: Inferring the type semantics of structured data. In: International Conference on Data Engineering (ICDE), pp. 206–217 (2013)Google Scholar
  17. 17.
    Calbimonte, J.-P., Corcho, O., Gray, A.J.G.: Enabling ontology-based access to streaming data sources. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 96–111. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Krötzsch, M., Rudolph, S., Hitzler, P.: Description logic rules. In: ECAI, pp. 80–84 (2008)Google Scholar
  19. 19.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)Google Scholar
  20. 20.
    Lutz, C.: Interval-based temporal reasoning with general tboxes. In: IJCAI, pp. 89–96 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Freddy Lécué
    • 1
  • Robert Tucker
    • 1
  • Veli Bicer
    • 1
  • Pierpaolo Tommasi
    • 1
  • Simone Tallevi-Diotallevi
    • 1
  • Marco Sbodio
    • 1
  1. 1.Smarter Cities Technology Centre, Damastown Industrial EstateIBM ResearchDublinIreland

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