Characterizing Traffic Density and Its Evolution through Moving Object Trajectories
Ahmed Kharrat, Karine Zeitouni, Iulian Sandu-Popa, Sami Faïz · 2009
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.