2009 Fifth International Conference on Signal Image Technology and Internet Based Systems (2009)
Nov. 29, 2009 to Dec. 4, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SITIS.2009.50
Managing and mining data derived from moving objects have become an important issue in recent years. In this paper, we are interested in mining trajectories of moving objects, such as vehicles in the road network. We propose a method for discovering dense routes by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatiotemporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph connecting dense routes over consecutive time periods. This graph is labeled by a degree of evolution. We have implemented and tested the proposed algorithms, which have shown their effectiveness and efficiency.
Moving object databases, spatiotemporal data mining, similarity, clustering, road traffic
I. Sandu-Popa, A. Kharrat, K. Zeitouni and S. Faiz, "Characterizing Traffic Density and Its Evolution through Moving Object Trajectories," 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems(SITIS), Marrakech, Morocco, 2009, pp. 257-263.