Event-Based System for Generation of Traffic Services in Road Congestions

  • C. Sotomayor-Martínez
  • R. Toledo-Moreo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


When traffic grows and a large number of vehicles meet on the same road stretch, any unusual behavior of a vehicle causes a change on the scene, pushing the rest of vehicles to adapt to the new situation. In these cases, the challenge is not only to predict conflictive situations, but also to do it in time. This paper focuses on the detection of traffic congestions by means of analyzing the behavior of the vehicles that conform a scene as independent events that influence one another. A Complex Event Processing (CEP) engine is employed for basic event processing and the establishment of relations among events, raising up the awareness of the situation as the linked events induce more and more complex ones. Once the status of the carriageway is known, the traffic center can determine what to do to reduce congestion and launches services such as drive speed commands or alternative routes.


Intelligent Transportation System Congestion Level Intelligent Vehicle Open Geospatial Consortium Complex Event Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Luckham, D., Schulte, R.: Event Processing Glossary - Version 1.1 (2008)Google Scholar
  2. 2.
    Dunkel, J., Fernandez, A., Ortiz, R., Ossowski, S.: Event-Driven Architecture for Decision Support in Traffic Management Systems. In: Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (2008)Google Scholar
  3. 3.
    Pawlowski, O., Dunkel, J., Bruns, R., Ossowski, S.: Applying Event Stream Processing on Traffic Problem Detection. In: 14th Portuguese Conference on Artificial Intelligence,EPIA (2009)Google Scholar
  4. 4.
    Open Geospatial Consortium (OGC),
  5. 5.
    EspetTech: Esper Reference Documentation.,
  6. 6.
    Toledo, R., Zamora, M.A.: Collision Avoidance Support in Road with Lateral and Longitudinal Maneuver Prediction by Fusing GPS/IMU and Digital Maps. In: Transportation Research Part C: Emerging Technologies, vol. 18(4), pp. 611–625 (2010)Google Scholar
  7. 7.
    Toledo-Moreo, R., Zamora-Izquierdo, M.A., Ubeda-Miñarro, B., Gomez-Skarmeta, A.F.: High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS. IEEE Transactions on Intelligent Transportation Systems 8(3), 491–511 (2007)CrossRefGoogle Scholar
  8. 8.
    Barshan, B., Durrant-Whyte, H.F.: Inertial Navigation Systems for Mobile Robots. IEEE Internatinal Transactions on Robotics and Automation II(3), 328–342 (1995)CrossRefGoogle Scholar
  9. 9.
    Toledo, R., Sotomayor, C., Gomez-Skarmeta, A.F.: Quadrant: An Architecture Design for Intelligent Vehicle Services in Road Scenarios. Monograph on Advances in Transport Systems Telematics, 451–460 (2006)Google Scholar
  10. 10.
    Kokar, M.M., Matheus, C.J., Letkowski, J.A.: Association in Level 2 fusion. In: SPIE (2004)Google Scholar
  11. 11.
    Ceruti, M.G.: Ontology for Level-One Sensor Fusion and Knowledge Discovery. In: SPIE (2004)Google Scholar
  12. 12.
    Matheus, C.J., Kokar, M.M., Baclawski, K.: A Core Ontology for Situation Awareness. In: Proceedings of Sixth International Conference on Information Fusion, Cairns, Australia, pp. 545–552 (2003)Google Scholar
  13. 13.
    Ceruti, M.G., Kamel, M.N.: Preprocessing and Integration of Data from Multiple Sources for Knowledge Discovery. International Journal on Artificial Intelligence Tools 8(3), 159–177 (1999)Google Scholar
  14. 14.
    Waltz, E., Llinas, J.: Multisensor Data Fusion. Artech House, Boston (1990)Google Scholar
  15. 15.
    Dance, S., Caelli, T., Liu, Z.Q.: An architecture for a traffic scene interpretation system. Technical Report 94/12, Dept. of Computer Science. University of Melbourne (1994)Google Scholar
  16. 16.
    Jakobson, G., Lewis, L., Buford, J.: An Approach to Integrated Cognitive Fusion. Fusion Conference (2004),
  17. 17.
    Sun, T.Y., Tsai, S.J., Tseng, J.Y., Tseng, Y.C.: The Study on Intelligent Vehicle Collision-Avoidance System with Vision Perception and Fuzzy Decision Making, Intelligent Vehicles Symposium. In: Proceedings IEEE, pp. 112–117 (2005)Google Scholar
  18. 18.
    Ammoun, S., Nashashibi, F., Laurgeau, C.: Real-time crash avoidance system on crossroads based on 802.11 devices and GPS receivers. In: Proceedings of the IEEE ITSC 2006, Toronto, Canada, pp. 1023–1028. IEEE, Los Alamitos (2006)Google Scholar
  19. 19.
    Busch, M., Blackman, S.: Evaluation of IMM filtering for an air defense system application. Signal and Data Processing of Small Targets. In: Proc. SPIE, vol. 2561, pp. 435–447 (1995)Google Scholar
  20. 20.
    SUMO: Simulation of Urban Mobility.,

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • C. Sotomayor-Martínez
    • 1
  • R. Toledo-Moreo
    • 2
  1. 1.DIIC, Faculty of Computer ScienceUniv. MurciaMurciaSpain
  2. 2.DETCP, Edif. AntigonesTechnical Univ. of CartagenaCartagenaSpain

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