Understanding Urban Human Mobility through Crowdsensed Data

TL;DR

This paper reviews recent research on urban human mobility using crowdsensed data, providing a taxonomy, tutorial, and case studies to guide future studies in this vital urban planning area.

Contribution

It offers a comprehensive review, taxonomy, and tutorial on crowdsensed data types and analysis methods, along with case studies demonstrating practical applications.

Findings

Effective matching of data types to mobility subjects.

Demonstrated city-wide and building-scale mobility analysis.

Contributions to specific data types and mobility categories.

Abstract

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.