Limits of Predictability in Human Mobility Chaoming Song 1,2 , Zehui Qu 1,2,3 , Nicholas Blumm 1,2 , Albert-Laszlo Barabasi 1,2* 1 Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA. 2 Department of Medicine, Harvard Medical School, and Center for Cancer Systems Biology, Dana- Farber Cancer Institute, Boston, MA 02115, USA. 3 School of Computer Science and Engineering, University of Electric Science and Technology of China, Chengdu 610054, China. Presenter: Rufeng Ma
Background Why do people study human mobility? Urban planning and traffic engineering human infectious disease
Curr rrent work rks Brockmann, Nature, 2006 It turns out that the distribution of traveling distances decays algebraically, and is well reproduced within a two parameter continuous-time random-walk model. Gonz´alez, Nature, 2008 In this case the distribution of displacements over all users is also well approximated by a truncated power-law and analyzed in terms of truncated Lévi flights, i.e., random walks with power-law distributed step sizes.
Obje jectiv ive A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable?
Data coll llectio ion Mobile carriers record the closest mobile tower each time the user uses his or her phone. • 50,000 individuals chosen from ~10 million anonymous • 3-month-long record • Visit more than 2 loations (tower vicinity) • Average call frequency f is >=0.5/hour
Data coll llectio ion A B 22 vicinities 76 vicinities Mobility networks associated with The trajectories of two users with the two users shown in Figure A sidely different mobility patterns.
Entropy Entropy is probably the most fundamental quantity capturing the degree of predictability characterizing a time series. • The random entropy 𝑠𝑏𝑜𝑒 ≡ 𝑚𝑝 2 𝑂 𝑗 𝑇 𝑗 • The temporal-uncorrelated entropy 𝑂𝑗 𝑣𝑜𝑑 ≡ − 𝑇 𝑗 𝑞 𝑗 𝑘 𝑚𝑝 2 𝑞 𝑗 (𝑘) 𝑘=1 • The actual entropy ′ 𝑚𝑝 2 [𝑄(𝑈 𝑗 ′ )] 𝐵𝑇 𝑗 = − 𝑄 𝑈 𝑗 ′ ⊂𝑈 𝑗 𝑈 𝑗
In Incomple leteness Users tend to place most of their calls in short bursts, followed by long periods with no call activity, during which we have no information about the user’s location. Distribution of the time intervals Distribution of the fraction of between consecutive calls τ unknown locations
Analy lysis is Distribution of entropy Distribution of the predictability
Analy lysis is The dependence of predictability The fraction of time a user spends Π 𝑛𝑏𝑦 on the user’s radius of gyration in the top n most visited locations
Analy lysis is The average number of Hourly regularity over a The averaged 𝑆/𝑆 𝑠𝑏𝑜𝑒 visited locations N during week-long time period versus the radius of gyration each hourly time frame with in a week
Conclu lusio ion The authors explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual’s trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, they find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
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