One of the wide used modeling techniques applied to model human mobility patterns is synthetic mobility modeling. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable until now of generating realistic mobility models. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of synthetic mobility modeling. It reviews well-known approaches along with their pros and cons and finalizes with a qualitative comparison between them.
Top1. Introduction
Ubiquitous access to information anywhere, anytime and anyhow is a main feature of future communication networks which experience a tremendous development clearly evident from the wide variety of new services and broadband applications way beyond the classical application, i.e. telephony (Diab & Mitschele-Thiel, 2014). The Internet itself emerges towards what is today termed “the Internet of things”, which assumes that users of future communication networks are objects, not only humans. Each object is embedded with sensors and is able to communicate. In other words, the physical world itself including communication networks, humans and objects is becoming a type of huge information system. To satisfy always increasing demands of users within such communication and information systems, the better support of mobility, the provision of services at low costs, etc. are the drivers standing behind the standardization of 4th Generation (4G) mobile communication networks, termed Long Term Evolution (LTE). Researchers expect that the number of LTE subscribers will reach the base of 3G/UMTS networks (1.087 million subscribers) by the year 2015 (Garza, Ashai, Monturus & Syputa, 2010).
One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have myriad uses in crucial fields such as mobile communication research, urban planning, ecology, epidemiology, etc. The authors in (Gonzalez, Hidalgo & Barabasi, 2008) state that human mobility patterns are far from being random. They show a high degree of temporal and spatial regularity, thus, follow simple reproducible patterns. So, faithfully reproduction of the movements of real people significantly helps in answering crucial questions necessary to improve the experience of individuals, organizations, etc. For instance, an accurate reproduction of how people move around a city can help to evaluate if the installation of a specific sensing application on mobile devices would be able to reach the desired coverage, see (Isaacman, et al., 2012).
The modeling of how large as well as small populations move within small, medium and metropolitan areas is the goal that various human mobility modeling techniques tried to achieve. The modeling techniques tried to capture how humans move between places that have special importance in their life, e.g. work places, homes, etc. Many studies have shown that individuals tend to spend the majority of their time at few such places, see (Gonzalez, Hidalgo & Barabasi, 2008), (Isaacman, et al. 2011) and (Song, Qu, Blumm & Barabási, 2010). Keep in mind that the reproduction of human density over time at various geographic scales helps in addressing essential societal issues like the environmental impact of home-to-work commutes for instance. Furthermore, different geographic distributions of communities, commercial centers, companies, etc. heavily affect human mobility patterns, which are affected by and also must be reflected in various factors such as transportation infrastructures, plans for cities growth, etc. Previous efforts have reported significant differences between cities in terms of metrics such as commute distances for instance, see (Isaacman, et al. 2011a), (Isaacman, Becker, Cáceres, Kobourov, Rowland & Varshavsky, 2010) and (Noulas, Scellato, Lambiotte, Pontil & Mascolo 2012).