Modeling the Social, Spatial, and Temporal dimensions of Human Mobility in a unifying framework

Modeling human mobility is important in the context of smart cities as it can assist design of the pervasive systems and intelligent services in the city. In synthetic mobility models dynamic processes in the city are modeled by means of either simulation or mathematical analysis. Traditional approaches to synthetic mobility modeling are usually bounded by the state of the art findings in human mobility analysis and fail to adopt when the new results come up from trace analysis. On the other hand, understanding of the dependencies between different mobility characteristics is currently missing in existing models. This implies that there is no direct way for controlling the output of the models (e.g., statistics of contacts between people) from the input parameters (e.g., structure of social graph, human mobility patterns). In this work we propose a mobility framework which can be instantiated to the required mobility settings and produce controllable output. The framework is built around the three dimensions of human movements, namely, social, spatial and temporal. The social environment in the framework is customized by taking the social graph as input. Then the spatial dimensions is added by distributing communities of tightly connected users across common meeting places and assigning them to physical location. The temporal dimension of human arrivals to places is modeled with the stochastic processes. We test the framework flexibility by showing that it can reproduce the mobility behavior observed in the realistic traces from the online location-based social network Gowalla. Additionally, we show that the framework can reproduce controllable output by providing thorough mathematical analysis of the contact statistics in different mobility scenarios.