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Approaching the limit of predictability in human mobility.
Xin Lu1, Erik Wetter, Nita Bharti
11] College of Information System and Management, National University of Defense Technology, 410073 Changsha, China [2] Flowminder Foundation, 17177 Stockholm, Sweden [3] Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden [4] Department of Sociology, Stockholm University, 17177 Stockholm, Sweden.
Scientific Reports
|October 12, 2013
Summary
Human mobility in Cote d'Ivoire is highly predictable, reaching up to 88% theoretical limits. Markov chain models achieve high accuracy in predicting travel patterns, demonstrating the impact of historical behavior on movement.
Area of Science:
- Computational social science
- Mobility data analysis
- Predictive modeling
Background:
- Understanding human mobility patterns is crucial for urban planning and resource allocation.
- Mobile phone data offers a rich source for analyzing large-scale travel behaviors.
- Quantifying movement predictability and its underlying factors remains an active research area.
Purpose of the Study:
- To analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records.
- To measure movement uncertainties using entropy, considering trajectory frequencies and temporal correlations.
- To evaluate the accuracy of Markov chain (MC) based models in predicting human mobility.
Main Methods:
- Utilized mobile phone call data records from 500,000 individuals.
- Employed entropy to quantify movement uncertainty and analyzed trajectory frequencies and temporal correlations.
- Implemented and assessed multiple Markov chain (MC) based models for location prediction.
Main Results:
- Theoretical maximum predictability of human movement was found to be as high as 88%.
- Markov chain models achieved prediction accuracies of 87% for stationary trajectories and 95% for non-stationary trajectories.
- Human mobility is significantly influenced by historical behavioral patterns.
Conclusions:
- Human mobility exhibits high predictability, closely approaching theoretical limits.
- Markov chain models provide a robust and accurate method for predicting individual travel patterns.
- Understanding historical behavior is key to unlocking the predictive power of mobility data.
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