Managing Sensor Data on Urban Traffic
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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.
KeywordsSensor Data Street Segment Occupancy Rate Intelligent Transportation System Fundamental Diagram
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- 1.CADDY: The CADDY Website - (2007), http://norma.mas.ecp.fr/wikimas/Caddy
- 2.Scemama, G., Carles, O.: Claire-SITI, Public road Transport Network Management Control: a Unified Approach. In: 12th IEEE Int. Conf. on Road Transport Information and Control (RTIC 2004) (2004)Google Scholar
- 3.Joliveau, M.: 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 Paris) (2008)Google Scholar
- 5.Joliveau, M., Vuyst, F.D.: Space-time summarization of multisensor time series. case of missing data. In: Int. Workshop on Spatial and Spatio-temporal data mining, IEEE SSTDM (2007)Google Scholar
- 7.Hugueney, B.: 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. 542–552 (2006)Google Scholar
- 8.Hugueney, B., Joliveau, M., Jomier, G., Manouvrier, M., Naja, Y., Scemama, G., Steffan, L.: Towards a data warehouse for urban traffic (in french). Revue des Nouvelles Technologies de L’Information RNTI (B2), 119–137 (2006)Google Scholar
- 9.Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norm. In: Proc. of the 26th VLBD Conference, pp. 385–394 (2000)Google Scholar
- 10.Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems (2000)Google Scholar
- 11.Mariotte, L., Medeiros, C.B., Torres, R.: Diagnosing Similarity of Oscillation Trends in Time Series. In: International Workshop on spatial and spatio-temporal data mining - SSTDM, pp. 243–248 (2007)Google Scholar
- 13.Kriegel, H.P., Kröger, P., Kunath, P., Renz, M., Schmidt, T.: Proximity queries in large traffic networks. In: Proc. ACM GIS, pp. 1–8 (2007)Google Scholar