  In this paper, we present a comprehensive survey of human-mobility modeling
based on 1680 articles published between 1999 and 2019, which can serve as a
roadmap for research and practice in this area. Mobility modeling research has
accelerated the advancement of several fields of studies such as urban
planning, epidemic modeling, traffic engineering and contributed to the
development of location-based services. However, while the application of
mobility models in different domains has increased, the credibility of the
research results has decreased. We highlight two significant shortfalls
commonly observed in our reviewed studies: (1) data-agnostic model selection
resulting in a poor tradeoff between accuracy vs. complexity, and (2) failure
to identify the source of empirical gains, due to adoption of inaccurate
validation methodologies. We also observe troubling trends with respect to
application of Markov model variants for modeling mobility, despite the
questionable association of Markov processes and human-mobility dynamics. To
this end, we propose a data-driven mobility-modeling framework that quantifies
the characteristics of a dataset based on four mobility meta-attributes, in
order to select the most appropriate prediction algorithm. Experimental
evaluations on three real-world mobility datasets based on a rigorous
validation methodology demonstrate our frameworks ability to correctly analyze
the model accuracy vs. complexity tradeoff. We offer these results to the
community along with the tools and the literature meta-data in order to improve
the reliability and credibility of human mobility modeling research.
