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Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.
Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an
This chapter introduces OpenStreetMap - a crowd-sourced, worldwide mapping project and geospatial data repository - to illustrate its usefulness in quickly and easily analyzing and visualizing planning and design outcomes in the built environment. It
Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved loc
The research objectives are exploring characteristics of human mobility patterns, subsequently modelling them mathematically depending on inter-event time and traveled distances parameters using CDRs (Call Detailed Records). The observations are obta
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