Clustering-based Event Detection from GPS Track Data
Sungsoon Hwang, Timothy Hanke, Christian Evans
Journal of Data Processing, Vol. 3, No. 3, 2013.
DOI: 
Link: http://www.dline.info/jdp/fulltext/v3n3/3.pdf
Abstract:
This paper considers accuracy of detecting geographic events (significant trips and stops made) from GPS track data. The proposed method modifies DBSCAN, a density-based spatial clustering algorithm, to temporal criteria to detect stops as spatiotemporal clusters, and uses temporal filtering to smooth out any misclassified values. Percent correctly classified based on the proposed method is 94% with kappa index .88. Experimentation results indicate that a clusteringbased event detection algorithm combined with smoothing techniques provides a relatively reliable means to infer events from noisy GPS track data.