Complex network study on moving object trajectories

Although we have a massive amount of data on moving object trajectories, which have been easily collected by geographic positioning devices, it is still a major challenge to process such information in order to generate knowledge about the behavior of these objects. In this paper, we study moving object trajectories from a complex network point of view. First, we consider a network with a node representing one trajectory and an edge representing an encounter between two trajectories in time and space. Real data were gathered from vehicles moving within Milan's city center, in a total of 225,932 trajectories. We have generated 7 networks denoting each day of the week. Our analysis shows that for all computed networks the degree distribution decays exponentially. We determine the exact values of the clustering coefficient, and the average shortest path length and compare them with those of the Erdo ?s-Re ?nyi. The time evolution of these quantities are calculated and the corresponding results discussed.