Modelling the scaling properties of human mobility

By Chaoming Song, Tal Koren, Pu Wang, Albert-László Barabási. 2010

1,332 citations recorded by the source corpus.

Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/1ce456ef68d882dd7846c187d16d494e33dd4daa

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, background): “EPR and its extensions focus mainly on the spatial aspects of human mobility, implementing unrealistic temporal mechanisms.”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, background): “Such a problem is in fact similar to traditional clustering problems in Wireless Sensor Networks (WSNs) [155], targeting, in this case, the formation, management, and ending of a fog domain.”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “In (Song et al., 2010) authors investigated to what degree is human behavior predictable with results indicating that the development of accurate predictive models is a scientiﬁcally grounded possibility, with potential impact on our well-being and public health.”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “…models that randomly draw the stay duration (or waiting time Δ t ) or the number of visited locations ( N ) from aggregated empirical distributions (14, 15, 29), by introducing three individual-specific parameters including weekly home-based tour number n w , dwell rate β 1 , and burst rate β 2 ,…”
Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “Since each trip can be viewed as a displacement in the random walk model, the distributions of the distance and direction of the extracted trips are investigated in this research.”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “However, despite some degree of change and spontaneity, human mobility is, in fact, characterized by a deep-rooted regularity that allows us to detect predictable trends of urban dynamics [43, 42, 19, 15, 4].”
Unravelling daily human mobility motifs (2013, background): “Such behaviour for large time scales can be reproduced by an exploration and preferential return model with the displacement distribution as an input [9] which can be used to model epidemic spreading on the airline network [14].”
Understanding Human Mobility from Twitter (2014, background): “ P(L) can be described by a power-law function P(L) ˘L . It has been recently suggested that the Zipf’s law of the visitation frequency in human mobility is rooted in the preferential return dynamics [14], i.e. humans have a tendency to return to the locations they visited frequently before. In this”
The science of science: from the perspective of complex systems (2017, background): “Thus far, numerous models have been developed to reproduce the statistical patterns that are extracted from empirical human mobility data [20,22,23,25].”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “…(Chowell et al. 2003), big data are more accurate, objective, plentiful and highly accessible, providing opportunities to better describe and understand human mobility (Lu and Liu 2012, Brockmann, Hufnagel and Geisel 2006, Rhee et al. 2011, Gonzalez, Hidalgo and Barabasi 2008, Song et al. 2010).”
Predictability of population displacement after the 2010 Haiti earthquake (2012, background): “Pioneering work using mobile phone data to describe human mobility patterns has been carried out during stable social conditions (17-19).”
Human mobility: Models and applications (2017, methodology): “This result provides further insight into studies using on bank note and mobile phone data [46, 48, 81] in that the power-law behavior of the distribution of jump lengths is the result of combining several distinct log-normal distributions, corresponding to the different transportation modes availab”
Human mobility models for opportunistic networks (2011, background): “A thorough study of individual movements became possible only recently with the availability of real life mobility traces collected by cell phone operators [3][4], academic experiments [5], and Internet communities [6].”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “Later, large data sets of mobile phone call records were analyzed, to the purpose of discovering and validating the macro-level laws of human mobility, such as the law governing the distribution of traveled distances [19,36].”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “returning to particular points, [36]), geographical environments [30,37–39], and distance effects [3,9,40].”