Human Mobility Modelling Based on Dense Transit Areas Detection with Opportunistic Sensing

Abstract EN

With the advent of smartphones, opportunistic mobile crowdsensing has become an instrumental approach to perceive large-scale urban dynamics.

In this context, the present work presents a novel approach based on such a sensing paradigm to automatically identify and monitor the areas of a city comprising most of the human transit.

Unlike previous approaches, the system performs such detection in real time at the same time the opportunistic sensing is carried out.

Furthermore, a novel multilayered grill partitioning to represent such areas is stated.

Finally, the proposal is evaluated by means of a real-world dataset.