Approaching the Limit of Predictability in Human Mobility

By Xin Lu, Erik Wetter, N. Bharti, Andrew J. Tatem, Linus Bengtsson. Scientific Reports, 2013

390 citations recorded by the source corpus.

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our 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.

Subfields: Epidemiology, Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/543d37c5e4dc2d7e9f6d359833b8228aece7c0a3

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Citations in the corpus

The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, result): “Similar results are found by other researchers (Lu et al., 2012, 2013).”
Human mobility: Models and applications (2017, neutral): “The raw data consists of a user ID, date and time stamp of the communication and the sub-prefecture number (1-255) which can be linked to a file containing the latitude and longitude of the center of each sub-prefecture [51].”
Dynamic population mapping using mobile phone data (2014, methodology): “MP call detail records were used in the past for studying human mobility patterns at the individual level (37 – 39) or for mapping human movements and activities using aggregated data (40 – 44).”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “Next to traditional census sources or transportation statistics, several novel approaches to quantifying human movements have become recently available that increase our understanding of mobility patterns [21–28,49–52].”
Big Data for Development: A Review of Promises and Challenges (2016, positive): “…phone call records allow to approach the limit of extractable predictability in human mobility, being able to predict up to 95 % of people’s movements in more stable situations (Song et al., 2010; Lu et al., 2013) and even 85 % in chaotic situations, such after an earthquake (Lu et al., 2012).”