Continuous Moving Object Clustering in Dynamic Road Network

Location based updation and clustering is important issue in dynamic road network. In dynamic road network moving object clustering is needed because huge number of moving objects are moving in the constrained road network. In this paper introduced new clustering algorithm for grouping similar objects in dynamic road network. The proposed clustering method is called density-based clustering. In this work the spatial and temporal objects are clustered separated and combined as per the requirements. The spatial distance between the moving objects are calculated with the help of Non-Ecludeian Distance. The Experiment is conducted using Geo-life GPS trajectory data and results are compared with K-mean moving object clustering (K-MCL) and moving object clustering methods (MC). The results are compared using accuracy, updation cost and speed of clustering parameters.