Jones, Jessica (Sandia National Laboratories), Wilson, Andrew (Sandia National Laboratories), Gooding, Renee (Sandia National Laboratories), DeLayo, Daniel (Stony Brook University), Dalbey, Keith (Sandia National Laboratories), Whetzel, Jonathan (Sandia National Laboratories), Sharan, Nitin (Sandia National Laboratories)

Behavioral Segmentation and Clustering of Geospatial Trajectories

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

The rapid growth of global positioning system (GPS) devices has led to a corresponding increase in the size of GPS datasets. While these large GPS datasets contain a wealth of information about the moving objects in them, manual classification and anomaly detection are prohibitively time consuming. We utilize unsupervised machine learning techniques to first identify the behaviors for individual moving objects and then cluster those objects by their behavioral sequences. In this way, trajectories behaving unusually as well as common patterns of behavior are both detectable in large datasets without requiring an a priori definition of “unusual” or “common.”