Exploring space–time structure of human mobility in urban space

Uncovering urban human mobility from large scale taxi GPS data

“…The fast development of information and communication technology makes it possible to understand travel behaviors of people by providing large-scale and granular data recording individual information chronologically. Various dataset including wireless network traces [1], GPS traces from probe vehicle data [2][3][4][5], mobile phone [6][7][8][9][10][11][12][13][14][15] and banking notes [16] are collected to study spatial-temporal feature of human movement. Jiang et al [2] analyzed the human mobility pattern from over 72 000 people's moving trajectories collected from 50 taxicabs during sixmonth.…”

“…Csáji et al [6] used principal component analysis to reveal the relation between features of human behavior and their geographical location from mobile phone dataset. Sun et al [7] also applied principal component analysis to discover the urban dynamics based on cell phones location information. Kang et al [8] presented the distribution of human urban travel followed the exponential law, in which the exponents were affected by city size and shape, and they used Monte Carlo simulation to verify the relation between intra-urban human mobility and urban.…”

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

“…Much research has been conducted to investigate the spatio-temporal patterns of urban scale human motion. Related applications include studies in cities such as Tallinn of Estonia (Ahas, Aasa, Silm, & Tiru, 2010), Milan (Ratti et al, 2006) and Rome of Italy (Sevtsuk & Ratti, 2010), and Hong Kong (Jiang & Liu, 2009), Wuhan (Li, Zhang, Wang, & Zeng, 2011) and Shenzhen of China (Sun, Yuan, Wang, Si, & Shan, 2011).…”

Intelligent bus routing with heterogeneous human mobility patterns

“…So if a person taking taxi wants to take bus instead, then the nearest bus stop will be his/her first choice. This partition method effectively describes the travel demand around bus stops comparing to other partition methods, such as grid-based partition [36] and road-network-based partition [43]. In the following sections, we use S to represent both stops and their, respectively, associated regions.…”