Nearest Neighbour Classification for Trajectory Data

Abstract

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes a nearest neighbour based trajectory data as two-step process. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy). In our method first, we build a classifier from the pre-processed 03 days training trajectory data and then we classify 04 days test trajectory data using class label. The resultant figure shows the our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics. © Springer-Verlag Berlin Heidelberg 2010.