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.
In these parts we presented different studies and findings about Human Mobility. According to reviewed findings, we distinguish three different baselines in Human Mobility studies. The first categories belongs to trajectory-base studies, which is mainly on analyzing trajectories of individuals. In these set of approaches, statistical measures such as jump length and radius of gyration are analyzed in order to find patterns and models for individuals movement. The second category is studying dynamic proximity networks of people in mobile ad hoc networks. In such a network , finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters @cite_26 . The third category of Human Mobility studies related to investigating flow on networks such as road network, transportation network, infrastructure networks and etc. Figure 1 illustrates three different baselines that we have distinguished for Human Mobility studies.
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Mobile ad hoc networks enable communications between clouds of mobile devices without the need for a preexisting infrastructure. One of their most interesting evolutions are opportunistic networks, whose goal is to also enable communication in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronously in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (e.g., smartphones and tablets) carried by human users, which is the typical OppNets scenario. In such a network where the movements of the communicating devices mirror those of their owners, finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this article we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start by reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobility. We discuss the shortcomings of the existing knowledge about human movements and extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that provides the basis for a discussion on open problems and further directions for research on modeling human mobility.