Limits of Predictability in Human Mobility

By Chaoming Song, Zehui Qu, Nicholas Blumm, Albert-László Barabási. Science, 2010

3,334 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/bc6c9a3a6147e4cbc855e45257ae6539cf96b9d5

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Collective Response of Human Populations to Large-Scale Emergencies (2011, positive): “These results offer a quantitative view of behavioral changes in human activity under extreme conditions, with potential long-term impact on emergency detection and response.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “Moreover, automatically detected visit and travel behaviors can help studies of human spatial and temporal behavior, and support research for urban planning, sustainability, epidemics, and health care [9, 28].”
TUBE: time-dependent pricing for mobile data (2012, background): “However, user mobility can often be predicted from location history [22]; thus, the user device can predict its location over the next day and pull prices from the appropriate base stations.”
Inferring human mobility patterns from taxicab location traces (2013, background): “Human mobility patterns have been well studied in the past [7, 11, 16, 18, 20, 6, 22].”
Recommender Systems (2012, background): “3 Accurate recommendation asks for both the high predictability of human movements [49,50] and quantitative way to define similarities between locations and people [51,52].”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “Although we acknowledge that this density estimation suﬀers due to the low number of samples, our measurements suggest that real user mobility is underestimated in studies based solely on CDRs, such as in [38], as they fail to capture travels outside of the covered area.”
Anonymization of location data does not work: a large-scale measurement study (2011, methodology): “Human mobility has been shownn to be highly predictable in [28] and [24].”
Friendship and mobility: user movement in location-based social networks (2011, background): “Even though the above are some of the most fundamental questions and hypotheses about the dynamics of human mobility, answers to them remain largely unknown mostly due to the fact that reliable large scale human mobility data has been hard to obtain.”
On the validity of geosocial mobility traces (2013, methodology): “We built a smartphone application (for both Android and iOS platforms), which generates two matching traces: a per-minute GPS trace of the user’s location, and a trace of the user’s checkin events polled using Foursquare’s open API.”
Exploring human mobility with multi-source data at extremely large metropolitan scales (2014, background): “But based on our empirical results in Section 3, an approach based on data from one type of transit has a bias against residents using other types of transit.”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, background): “ously visited locations and explorations of new places as a general and universal feature [18], [22], [23], [24].”
A literature survey on smart cities (2015, background): “Song et al. [103] analyzed the predictability of human spatial activity patterns by using mobile communication record data.”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, background): “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 ”