Human Mobility has attracted attentions from dierent elds of studies such as epidemic modeling, trac engineering, trac prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diusion and ow in the network and makes it easier to estimate how much one part of the network inuences another by using metrics like centrality measures. We aim to study population ow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of dierent data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges.
We report on our experience scaling up the Mobile Millennium traffic information system using cloud computing and the Spark cluster computing framework. Mobile Millennium uses machine learning to infer traffic conditions for large metropolitan areas from crowdsourced data, and Spark was specifically designed to support such applications. Many studies of cloud computing frameworks have demonstrated scalability and performance improvements for simple machine learning algorithms. Our experience implementing a real-world machine learning-based application corroborates such benefits, but we also encountered several challenges that have not been widely reported. These include: managing large parameter vectors, using memory efficiently, and integrating with the application's existing storage infrastructure. This paper describes these challenges and the changes they required in both the Spark framework and the Mobile Millennium software. While we focus on a system for traffic estimation, we believe that the lessons learned are applicable to other machine learning-based applications. The central points of communication network flow have often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), where non-central vertices get a comparatively large portion of the traffic.