Article by Takahiro Yabe et al: “The proliferation of large-scale, passively collected location data from mobile devices has enabled researchers to gain valuable insights into various societal phenomena. In particular, research into the science of human mobility has become increasingly critical thanks to its interdisciplinary effects in various fields, including urban planning, transportation engineering, public health, disaster management, and economic analysis. Researchers in the computational social science, complex systems, and behavioral science communities have used such granular mobility data to uncover universal laws and theories governing individual and collective human behavior. Moreover, computer science researchers have focused on developing computational and machine learning models capable of predicting complex behavior patterns in urban environments. Prominent papers include pattern-based and deep learning approaches to next-location prediction and physics-inspired approaches to flow prediction and generation.
Regardless of the research problem of interest, human mobility datasets often come with substantial limitations. Existing publicly available datasets are often small, limited to specific transport modes, or geographically restricted, owing to the lack of open-source and large-scale human mobility datasets caused by privacy concerns…(More)”.