Cities grow in a bottom-up manner, leading to fractal-like urban morphologies characterized by scaling laws. The correlated percolation model has succeeded modeling geometries imposing strong spatial correlations; however, the origin of underlying mechanisms behind spatially growth remains largely unknown. Our understanding human movements recently been revolutionized thanks increasing availabi...

Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of t...

The research objectives are exploring characteristics of human mobility patterns, subsequently modelling them mathematically depending on inter-event time ∆t and traveled distances ∆rparameters using CDRs (Call Detailed Records). The observations are obtained from Armada festival in France. Understanding, modelling and simulating human mobility among urban regions is excitement approach, due to...

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others....

BACKGROUND Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies an...

Most location-based services provide recommendations based on a user’s current location or a given route or destination. Even though there are indications that human movement is highly predictable, daily and weekly routines of individual users constitute a largely unexplored and unexploited area. In this paper, we show and discuss such patterns, based on a large dataset of GPS trajectories. In ...