Predicting the Next Location for Trajectories From Stolen Vehicles

In this article, we consider the External Sensor Trajectory Prediction problem for stolen vehicle trajectories. This analysis brings new challenges to the problem, as crime patterns are dynamic and drivers of stolen vehicles tend to move away from the sensors, which increases data dispersion. We analyze the effectiveness of different machine learning models and propose semantic enrichment with criminal data and points of interest to solve our problem. We also investigate the best attributes to improve EST prediction models, and how different spatial level representations can leverage prediction accuracy.