Managing Sensor Data On Urban Traffic

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 (embarked) sensors, generating large and complex spatio-temporal series. Research efforts in handling these data range from pattern matching and data mining techniques (for forecasting and trend analysis) to work on database queries (e.g., to construct scenarios). Work on embarked sensors also considers issues on trajectories and moving objects. This paper presents 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 database procedures to query these data. The first component is geared towards supporting pattern matching, whereas the second deals with spatio-temporal database issues. 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 test conducted on 1000 sensors, during 3 years, in a large French city. © 2008 Springer Berlin Heidelberg.5232 LNCS385394(2007) TheCADDYWebsite, , http://norma.mas.ecp.fr/wikimas/Caddy, CADDYScemama, G., Carles, O., Claire-SITI, Public road Transport Network Management Control: A Unified Approach (2004) 12th IEEE Int. Conf. on Road Transport Information and Control (RTICJoliveau, M., (2008) Reduction of Urban Traffic Time Series from Georeferenced Sensors, and extraction of Spatio-temporal series -in French, , Ph.D thesis, Ecole Centrale Des Arts Et Manufactures Ecole Centrale de ParisJolliffe, I., (1986) Principal Component Analysis, , Springer, New YorkJoliveau, M., Vuyst, F.D., Space-time summarization of multisensor time series. case of missing data (2007) Int. Workshop on Spatial and Spatio-temporal data mining, IEEE SSTDMDempster, A., Laird, N., Rubin, D., Maximum likelihood for incomplete data via the em algorithm (1977) Journal of the Royal Statistical Society series B, 39, pp. 1-38Hugueney, B., Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures (2006) Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 542-552Hugueney, B., Joliveau, M., Jomier, G., Manouvrier, M., Naja, Y., Scemama, G., Steffan, L., Towards a data warehouse for urban traffic (in french) (2006) Revue des Nouvelles Technologies de L'Information RNTI (B2), pp. 119-137Yi, B.K., Faloutsos, C., Fast time sequence indexing for arbitrary Lp norm (2000) Proc. of the 26th VLBD Conference, pp. 385-394Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S., (2000) Dimensionality reduction for fast similarity search in large time series databases, , Journal of Knowledge and Information SystemsMariotte, L., Medeiros, C.B., Torres, R., Diagnosing Similarity of Oscillation Trends in Time Series (2007) International Workshop on spatial and spatio-temporal data mining -SSTDM, pp. 243-248Mautora, T., Naudin, E., Arcs-states models for the vehicle routing problem with time windows and related problems (2007) Computers and Operations Research, 34, pp. 1061-1084Kriegel, H.P., Kröger, P., Kunath, P., Renz, M., Schmidt, T., Proximity queries in large traffic networks (2007) Proc. ACM GIS, pp. 1-8Kim, K., Lopez, M., Leutenegger, S., Li, K., A Network-based Indexing Method for Trajectories of Moving Objects (2006) LNCS, 4243, pp. 344-353. , Yakhno, T, Neuhold, E.J, eds, ADVIS 2006, Springer, HeidelbergGuting, R., Bohlen, M., Erwig, E., Jensen, C., Lorentzos, N., Schneider, M., Vazirgianis, M., A Foundation for Representing and Querying Moving Objects (2000) ACM Transactions on Database Systems, 25 (2), pp. 1-42Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C., A conceptual view on trajectories (2008) Knowledge and Data Engineering, 65 (1), pp. 126-14