Spatiotemporal Patterns of Urban Human Mobility

By Samiul Hasan, Christian M. Schneider, Satish V. Ukkusuri, Marta C. González. Journal of statistical physics, 2012

295 citations recorded by the source corpus.

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.

Subfields: Epidemiology, Transportation

View Paper: https://www.semanticscholar.org/paper/6ea7020bfde725f96ed6f7e1acfea625c942264e

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Citations in the corpus

Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (2016, background): “A large literature has been focused on extracting spatiotemporal activity patterns from large cellular network datasets [5], in combination with social media [11], leveraging model from statistical physics [10].”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, background): “…so far to improve our knowledge on human mobility at an unprecedented scale, informing us about the frequency and the number of visited locations over long-term observations (13 – 18), daily mobility networks of individuals (15, 19), and the distribution of trip distances (13, 15, 17, 20 – 22).”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “(c) User groups with different numbers of visited locations have the same fk distribution: fk ∼ k , which is similar to previous findings using larger spatial granularity [15, 42, 19].”
Understanding urban human activity and mobility patterns using large-scale location-based data from online social media (2013, background): “On the other hand, there are some recent mobility studies that have used distance-based measures to characterize human mobility patterns using alternative datasets collected from mobile phones [14], bank notes movements [15], and subway smart-card transactions [16, 17] etc.”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, unknown): “…Hidalgo, & Barabasi, 2008) Several more papers have used CDR data to understand spatio-temporal patterns of urban human mobility, routine behavior, and their predictability (e.g.,Hasan, Schneider, Ukkusuri, & Gonz ´ alez, 2013; Li, Gao, Lu, & Zhang, 2019; Song, Qu, Blumm, & Barab ´ asi, 2010).”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, background): “Despite these advantages, mobile phone data lacks information typically available from travel surveys about a respondent (e.g. age or income) or his/her trip (e.g. purpose or mode) (Richardson et al., 1995; Stopher and Greaves, 2007; Hu and Reuscher, 2004).”