, Volume 14, Issue 3, pp 279–305 | Cite as

Managing sensor traffic data and forecasting unusual behaviour propagation

  • Claudia Bauzer MedeirosEmail author
  • Marc Joliveau
  • Geneviève Jomier
  • Florian De Vuyst


Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.


Intelligent Transportation Systems Traffic sensor data Traffic modelling Sensor networks Time series 



This work was partially financed by CNPq (Brazil) and by the French Research Program “ACI Masse de Données 2003”. The authors thank INRETS (Laboratoire GRETIA) for providing real data and some of the problems discussed.


  1. 1.
    Agrawal R, Faloustos C, Swami A (1993) Efficient similarity search in sequence databases. In: Proc. 4th international conference on foundations of data organization and algorithms, pp 69–84Google Scholar
  2. 2.
    CADDY (2007) The CADDY Website.
  3. 3.
    Chan K, Fu AW (1999) Efficient time series matching by wavelets. In: Proc. 15th IEEE international conference on data engineering, pp 126–133Google Scholar
  4. 4.
    Cho H-J, Chung C-W (2005) An efficient and scalable approach to cnn queries in a road network. In: Proceedings 31st VLDB conference, pp 865–876Google Scholar
  5. 5.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood for incomplete data via the em algorithm. J R Stat Soc, Ser B 39:1–38Google Scholar
  6. 6.
    Faloutsos C (2002) Tutorial: sensor data mining: similarity search and pattern analysis. In: 28th international conference on very large data bases. Hong Kong, ChinaGoogle Scholar
  7. 7.
    Faloutsos C, Jagadish H, Mendelzon A, Milo T (1993) A signature technique for similarity based queries. In: Proc. of the international conference on compression and complexity of sequences, pp 2–20Google Scholar
  8. 8.
    Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. In: Proceedings 1994 ACM SIGMOD conference, Mineapolis, MN, pp 419–429Google Scholar
  9. 9.
    Guting R, Bohlen M, Erwig E, Jensen C, Lorentzos N, Schneider M, Vazirgianis M (2000) A foundation for representing and querying moving objects. ACM Trans Database Syst 25(2):1–42CrossRefGoogle Scholar
  10. 10.
    Han J, Kamber M (2002) Data mining: concepts and techniques. In: ACM SIGMOD, vol 31Google Scholar
  11. 11.
    Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu M (2000) Freespan: frequent pattern-projected sequential pattern mining. In: KDD ’00: proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, NY, USA, pp 355–359CrossRefGoogle Scholar
  12. 12.
    Hornsby KS, King K (2008) Modeling motion relations for moving objects on road networks. Geoinformatica 12(4):477–495CrossRefGoogle Scholar
  13. 13.
    Hugueney B (2003) Representations symboliques de longues series temporelles (Symbolic representations of long temporal series). PhD thesis, University Paris 6Google Scholar
  14. 14.
    Hugueney B (2006) Adaptive segmentation-based symbolic representations of time series for better modeling and lower bounding distance measures. In: Proc. 10th European conference on principles and practice of knowledge discovery in databases, pp 542–552Google Scholar
  15. 15.
    Joliveau M (2008) Reduction of urban traffic time series from georeferenced sensors, and extraction of spatio-temporal series—in French. PhD thesis, Ecole Centrale Des Arts Et Manufactures (Ecole Centrale de Paris)Google Scholar
  16. 16.
    Joliveau M, De Vuyst F (2008) Recherche de motifs spatio-temporels de cas atypiques pour le trafic routier urbain. In: Guillet F, et Trousse B (eds) Extraction et Gestion de Connaissances EGC 08, Revue des Nouvelles Technologies de l’Information—RNTI—E11. Cépaduès, Toulouse, pp 523–534Google Scholar
  17. 17.
    Joliveau M, Medeiros CB, Jomier G, De Vuyst F (2008) Managing sensor data on urban traffic. In: Proceedings 3rd SeCoGIS workshopGoogle Scholar
  18. 18.
    Joliveau M, De Vuyst F (2007) Space–time summarization of multisensor time series. Case of missing data. In: Int. workshop on spatial and spatio-temporal data mining, IEEE SSTDMGoogle Scholar
  19. 19.
    Jolliffe IT (1986) Principal component analysis. Springer, New YorkGoogle Scholar
  20. 20.
    Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2000) Dimensionality reduction for fast similarity search in large time series databases. J Knowl Inform Syst 3:263-286CrossRefGoogle Scholar
  21. 21.
    Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Locally adaptive dimensionality reduction for indexing large time serie databases. In: Proc. of the international IEEE conference on data mining (ICDM), pp 151–162Google Scholar
  22. 22.
    Keogh E, Chu S, Hart D, Pazzani M (2001) An online algorithm for segmentation time series. In: Proc. of ACM SIGMOD international conference, pp 289–296Google Scholar
  23. 23.
    Kim K, Lopez M, Leutenegger S, Li K (2006) A network-based indexing method for trajectories of moving objects. In: LNCS 4243, pp 344–353Google Scholar
  24. 24.
    Kriegel H-P, Kröger P, Kunath P, Renz M, Schmidt T (2007) Proximity queries in large traffic networks. In: Proc. ACM GIS, pp 1–8Google Scholar
  25. 25.
    Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: DMKD ’03: proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery. ACM Press, pp 2–11Google Scholar
  26. 26.
    Lochert C, Scheuermann B, Wewetzer C, Luebke A, Mauve M (2008) Data aggregation and roadside unit placement for a vanet traffic information system. In: Proceedings VANET’08. ACM Press, pp 58–65Google Scholar
  27. 27.
    Mariotte L, Medeiros CB, Torres R (2007) Diagnosing similarity of oscillation trends in time series. In: International workshop on spatial and spatio-temporal data mining—SSTDM, pp 243–248Google Scholar
  28. 28.
    Mautora T, Naudin E (2007) Arcs-states models for the vehicle routing problem with time windows and related problems. Comput Oper Res 34:1061–1084CrossRefGoogle Scholar
  29. 29.
    Medeiros CB, Carles O, Devuyst F, Hebrail G, Hugueney B, Joliveau M et al (2006) Towards a data warehouse for urban traffic (in French). Rev Nouv Technol Inf RNTI(B2):119–137Google Scholar
  30. 30.
    Scemama G, Carles O (2004) Claire-SITI, public road transport network management control: a unified approach. In: 12th IEEE int. conf. on road transport information and control (RTIC 04)Google Scholar
  31. 31.
    Shannon CE, Weaver W (1963) The mathematical theory of communication. University of Illinois Press, ChampaignGoogle Scholar
  32. 32.
    Shen W, Zhang HM (2009) On the morning commute problem in a corridor network with multiple bottlenecks: ITS system-optimal traffic flow patterns and the realizing tolling scheme. Transp Res, Part B 43:267–284CrossRefGoogle Scholar
  33. 33.
    Spaccapietra S, Parent C, Damiani ML, Macedo JA, Porto F, Vangenot, C (2008) A conceptual view on trajectories. Data Knowl Eng 65(1):126–146CrossRefGoogle Scholar
  34. 34.
    Stough R, Yang G (2003) Intelligent transportation systems. In: Eolss Publishers (ed) Encyclopedia of life support systems (EOLSS). Developed under the Auspices of the UNESCO, Oxford, UKGoogle Scholar
  35. 35.
    Tenenbaum JB, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323CrossRefGoogle Scholar
  36. 36.
    Tiesyte D, Jensen C (2008) Similarity-based prediction of travel times for vehicles traveling on known routes. In: Proceedings ACM GISGoogle Scholar
  37. 37.
    Yi BK, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norm. In: Proc. of the 26th VLBD conference, pp 385–394Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Claudia Bauzer Medeiros
    • 1
    Email author
  • Marc Joliveau
    • 2
  • Geneviève Jomier
    • 3
  • Florian De Vuyst
    • 4
  1. 1.ICUniversity of Campinas, UNICAMPCampinasBrazil
  2. 2.CIRRELTUniversité de MontréalMontréalCanada
  3. 3.LAMSADEUniversité Paris-DauphineParis Cedex 16France
  4. 4.Laboratoire Mathématiques Appliquées aux SystèmesEcole Centrale ParisChatenay-Malabry CedexFrance

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