Unravelling daily human mobility motifs

By Christian M. Schneider, Vitaly Belik, T. Couronné, Zbigniew Smoreda, Marta C. González. Journal of the Royal Society Interface, 2013

606 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/349a3cbb8c9fe9ab149ec25a0bc3b4e98b074d50

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Citations in the corpus

Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, methodology): “[41] by quantifying the motifs in daily trips using CDR data, also discovered various means by which individuals incorporated these stops/detours.”
From mobile phone data to the spatial structure of cities (2014, background): “[10] Schneider CM, Belik V, Couronn T, Smoreda Z, Gonzlez MC (2013) Unravelling daily human mobility motifs.”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, background): “At the same time, more recent studies using big data have started to inquire the underlying mechanisms that result in observed patterns (Jiang et al., 2009; Schneider et al., 2013).”
Human mobility: Models and applications (2017, methodology): “[44] used data from both mobile phone users and travel survey respondents to construct weekday mobility networks for each individual.”
Returners and explorers dichotomy in human mobility (2015, background): “All these studies document a stunning heterogeneity of human travel patterns that coexists with a high degree of predictability 28,29 : individuals exhibit a broad spectrum of mobility ranges while repeating daily schedules dictated by routine.”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, methodology): “If timing of the call activity is provided, one possible refined definition would be to identify as home location the tower cell with the largest activity during nighttime, and the work location as the one with the largest activity constrained to daytime (with variations of the definition of these i”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Big data have also drawn the attention of statistical physicists (Brockmann et al., 2006; Gonzalez et al., 2008; Schneider et al., 2013; Song et al., 2010) and computer scientists (Cho et al.”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “In Schneider et al. (2013), the authors associated daily mobility networks extracted from the sequence of trips in a day, called motifs , with trip chains extracted from travel diary surveys, and tried to associate a trip purpose by examining semantic-enriched land users surrounding destinations of…”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, methodology): “To uncover the key generating mechanisms needed to reproduce individual daily trajectories, we propose a time-inhomogeneous Markov chain model with three individualspecific parameters—weekly home-based tour number (nw), dwell rate (β1), and burst rate (β2)—to capture individual circadian propensity ”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, methodology): “The distributions of the 17 motifs extracted from cell phone data and the Massachusetts travel survey are similar, and also conform to previous findings in Paris and Chicago [39].”
A Survey on Deep Learning for Human Mobility (2020, background): “In an individual mobility network (IMN), nodes represent locations and directed edges represent an individual’s trips between locations [153, 163].”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, methodology): “[29], [35], and develop a pipeline of (1) parsing the CDR data to extract stay locations of phone users; (2) detecting phone users’ home location; (3) filtering users and select sta-”