Understanding Urban Human Mobility through Crowdsensed Data

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.

Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns

“…Mobility context detection is not a new topic in the research community; over the past decade, there have been a plethora of research works in this field [13], [14]. Most of these works exploits IMU sensors like accelerometer and gyroscope or the traces from other sensors like GPS and acoustic for fine grained mobility context (say, vehicle type) detection [15], [16]. However, despite all these successes in detecting the mobility just by using IMU sensors and GPS information, there has been a consistent criticism of these sensors for leaking out private information regarding the location of users [7].…”

Real-time GIS for smart cities

“…Real-time traffic data is a form of big data that captures human movements to provide a better understanding of human mobility (Gonzalez et al 2008), spatial-social structures (Deville et al 2016), and hidden patterns within human trajectories and daily activities (Bazzani et al 2011). Early human-mobility research relied heavily on Population and other Census data, which is expensive to collect, infrequently updated, and limited in amount and coverage (Zhou et al 2018). Recently, crowdsourcing, which generates locational data continuously from ubiquitous mobile devices such as smart phones, has yielded massive amounts of mobility data at unprecedented spatial and temporal scales (Stojanovic et al 2016).…”

Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis

“…In recent years, many applications have been proposed for different scenarios of urban data analysis, including transportation, the environment, energy, society, the economy, and public safety and security [12][13][14][15][16][17][18]. For example, a number of researchers have studied the store placement problem by focusing on various techniques, such as multiple regression discriminate analysis, spatial interaction models, and so on [19].…”