Online urban mobility detection based on velocity features

Abstract

The study of the mobility models that arise from the city dynamics has become instrumental to provide new urban services. In this context, many proposals applied an off-line learning on historical data. However, at the dawn of the Big Data era, there is an increasing need for systems and architectures able to process data in a timely manner. The present work introduces a novel approach for online mobility model detection along with a new concept for trajectory abstraction based on velocity features. Finally, the proposal is evaluated with a real-world dataset.