DeepAI AI Chat
Log In Sign Up

Understand Urban Human Mobility through Crowdsensed Data

by   Yuren Zhou, et al.

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.


page 2

page 5


Examining mobility data justice during 2017 Hurricane Harvey

Natural disasters can significantly disrupt human mobility in urban area...

Brief survey of Mobility Analyses based on Mobile Phone Datasets

This is a brief survey of the research performed by Grandata Labs in col...

Continuous Trajectory Generation Based on Two-Stage GAN

Simulating the human mobility and generating large-scale trajectories ar...

EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control

The coronavirus disease 2019 (COVID-19) outbreak has swept more than 180...

Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models

Call Detail Records (CDRs) are data recorded by telecommunications compa...

MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility

Collective urban mobility embodies the residents' local insights on the ...

Smallset Timelines: A Visual Representation of Data Preprocessing Decisions

Data preprocessing is a crucial stage in the data analysis pipeline, wit...