A REVIEW OF URBAN HUMAN MOBILITY RESEARCH BASED ON CROWD-SOURCED DATA AND SPACE-TIME AND SEMANTIC ANALYSIS

Abstract

Abstract. Detecting urban human mobility patterns helps contributes to many urbanization issues, such as urban planning and traffic management. With the growing volume of crowd-sourced data, many studies have benefited from this data type to explore people’s daily movements and track their activities. There are several published review papers examing these studies on urban human mobility, with the focus on defining models and applications. However, the absence of a review of studies on urban human mobility that considered spatial, temporal, and semantic properties as well as crowd-sourced data together has limited the proliferation of semantic content in addressing mobility issues. In response, this paper provides a review on urban human mobility, including the data, models, and applications used in the selected articles. We defined particular inclusion and exclusion criteria to select the most relevant articles. We also included metadata analysis to overview the existing relevant literature. Finally, several research challenges and open issues are discussed.