Limits of Predictability in Human Mobility

@article{Song2010LimitsOP,
  title={Limits of Predictability in Human Mobility},
  author={Chaoming Song and Zehui Qu and Nicholas Blumm and A L Barabasi},
  journal={Science},
  year={2010},
  volume={327},
  pages={1018 - 1021}
}
Predictable Travel Routines While people rarely perceive their actions to be random, current models of human activity are fundamentally stochastic. Processes that rely on human mobility patterns, like the prediction of new epidemics, traffic engineering, or city planning, could benefit from highly accurate predictive models. To investigate the predictability of human dynamics, Song et al. (p. 1018) used the recorded trajectories of millions of mobile phone users, collected by mobile phone… 

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References

SHOWING 1-10 OF 56 REFERENCES

Understanding individual human mobility patterns

The trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period is studied, finding that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity.

The origin of bursts and heavy tails in human dynamics

It is shown that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times.

Modeling bursts and heavy tails in human dynamics

It is shown that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times.

The scaling laws of human travel

It is shown that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and concluded that human travel on geographical scales is an ambivalent and effectively superdiffusive process.

Simulating dynamical features of escape panic

A model of pedestrian behaviour is used to investigate the mechanisms of panic and jamming by uncoordinated motion in crowds, and an optimal strategy for escape from a smoke-filled room is found, involving a mixture of individualistic behaviour and collective ‘herding’ instinct.

Understanding the Spreading Patterns of Mobile Phone Viruses

The mobility of mobile phone users is modeled in order to study the fundamental spreading patterns that characterize a mobile virus outbreak and it is found that although Bluetooth viruses can reach all susceptible handsets with time, they spread slowly because of human mobility, offering ample opportunities to deploy antiviral software.

Eigenbehaviors: identifying structure in routine

This work identifies the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population with the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network.

System of mobile agents to model social networks.

A model of mobile agents to construct social networks, based on a system of moving particles by keeping track of the collisions during their permanence in the system, finds the emergence of a giant cluster in the universality class of two-dimensional percolation.

Forecast and control of epidemics in a globalized world.

A probabilistic model is introduced that describes the worldwide spread of infectious diseases and shows that a forecast of the geographical spread of epidemics is indeed possible, taking into account national and international civil aviation traffic.
...