Limits of Predictability in Human Mobility

  title={Limits of Predictability in Human Mobility},
  author={Chaoming Song and Zehui Qu and Nicholas Blumm and A L Barabasi},
  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… 

Approaching the Limit of Predictability in Human Mobility

The findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.

Predictability of Irregular Human Mobility

It is found that the travel patterns of Europeans visiting for holidays are less predictable than those visiting for education, while East Asian visitors show the opposite patterns, ie, more predictable for holidays than for education.

Regularity and Predictability of Human Mobility in Personal Space

The results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

Predictability of Irregular Human Mobility Travel Pa erns of International and Domestic Visitors

Understanding human mobility is critical for decision support in areas from urban planning to infectious diseases control. Prior work has focused on tracking daily logs of outdoor mobility without

Understanding predictability and exploration in human mobility

This work investigates which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year, and shows that it is much easier to achieve high accuracy when predicting the time-bin location than when predicted the next place.

Modelling the scaling properties of human mobility

Empirical data is used to show that the predictions of the CTRW models are in systematic conflict with the empirical results, and two principles that govern human trajectories are introduced, allowing for a statistically self-consistent microscopic model for individual human mobility.

The scaling of human mobility by taxis is exponential

Predictability in Human Mobility based on Geographical-boundary-free and Long-time Social Media Data

  • Yuan LiaoSonia Yeh
  • Environmental Science, Computer Science
    2018 21st International Conference on Intelligent Transportation Systems (ITSC)
  • 2018
A dataset that captures Twitter users' mobility where they routinely visit a couple of regions at most of the time and occasionally explore new regions is revealed, revealing a 70% potential predictability.

Inferring human mobility using communication patterns

It is shown that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance, and it is argued that the strength of the model comes from directly incorporating the social dimension of mobility.

The impact of human mobility data scales and processing on movement predictability

It is found that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and the nonlinear dependencies are presented.



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.