Approaching the Limit of Predictability in Human Mobility

Lu, Xin; Wetter, Erik; Bharti, Nita; Tatem, Andrew J.; Bengtsson, Linus

doi:10.1038/srep02923

Cited by 308 publications

(262 citation statements)

References 22 publications

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“…Markov chains have been applied to a variety of data sets. Lu et al [18] applied Markov chain models to CDR-based locations in Cote D'Ivore, with a prediction goal of estimating the last location of the day at the prefecture (county) level. Under these conditions the models perform extremely well, reaching an accuracy of over 90%.…”

Section: Related Workmentioning

confidence: 99%

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Understanding predictability and exploration in human mobility

2018

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Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors -in terms of modeling approaches and spatio-temporal characteristics of the data sources -have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate 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. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

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Section: Related Workmentioning

confidence: 99%

“…Predicting a user's location with the precision of few meters is intuitively much more difficult that predicting with precision of several kilometers, cf. [18]. In order to examine the effect of spatial resolution, we also consider results for cell sizes of 500 meters and 5000 meters, and apply the Markov model.…”

Section: Next-cell Predictionmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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“…Data provided by communication tools are opening up new opportunities for studying sociospatial behaviors (33)(34)(35)(36). MP call detail records were used in the past for studying human mobility patterns at the individual level (37)(38)(39) or for mapping human movements and activities using aggregated data (40)(41)(42)(43)(44). Most of these studies focused on specific cities or city neighborhoods or groups, and were aimed at understanding traffic flows (40), mapping the intensity of human activities at different times (42)(43)(44), or exploring seasonality in foreign tourist numbers and destinations (45,46).…”

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confidence: 99%

Dynamic population mapping using mobile phone data

DeVillé

Linard

Martín

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.O ur knowledge of human population numbers and distribution for many areas of the world remains poor (1) despite their importance for policy (2, 3), operational decisions (4), and research (5-7) across many fields. In the 1990s, a growing interest in the global mapping of human populations emerged (8, 9), leading to the advanced development of methodologies that undertake the spatial downscaling of human population count data from censuses summarized over large and irregular administrative units to grid squares of 100 m to 5 km resolution (10-16). Initial efforts to downscale these data used simple areal weighting methods (10, 17) or dasymetric modeling approaches (13-15), which use ancillary layers to redistribute population counts within administrative units (18). Modeling techniques that spatially downscale population numbers into gridded datasets continue to be refined, with basic dasymetric models increasing in sophistication, incorporating multiscale remotely sensed and geospatial data and making improvements in the type of statistical algorithms used in the modeling process (19)(20)(21). These detailed population databases have proven crucial for studies reliant on information about human population distributions, typically for calculating populations at risk for human or natural disasters (22-24), to assess vulnerabilities (7, 25), or to derive hea...

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“…The goal of this article is to first review each of these key on-going developments, and then discuss opportunities and approaches of exploiting location-awareness at each network delivery level. This is motivated by recent analyses on human travel patterns indicating that people follow particular routes with a predictability of up to 88% [5]. These findings show that human mobility is highly dependent on historical behaviors.…”

Section: Introductionmentioning

confidence: 88%

Toward green media delivery: location-aware opportunities and approaches

Abou-zeid

Hassanein

2014

IEEE Wireless Commun.

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Mobile media has undoubtedly become the predominant source of traffic in wireless networks. The result is not only congestion and poor Quality-of-Experience, but also an unprecedented energy drain at both the network and user devices. In order to sustain this continued growth, novel disruptive paradigms of media delivery are urgently needed.We envision that two key contemporary advancements can be leveraged to develop greener media delivery platforms: 1) the proliferation of navigation hardware and software in mobile devices has created an era of location-awareness, where both the current and future user locations can be predicted; and 2) the rise of context-aware network architectures and self-organizing functionalities is enabling context signaling and in-network adaptation. With these developments in mind, this article investigates the opportunities of exploiting location-awareness to enable green end-to-end media delivery. In particular, we discuss and propose approaches for location-based adaptive video quality planning, innetwork caching, content prefetching, and long-term radio resource management. To provide insights on the energy savings, we then present a cross-layer framework that jointly optimizes resource allocation and multi-user video quality using location predictions. Finally, we highlight some of the future research directions for location-aware media delivery in the conclusion.

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Approaching the Limit of Predictability in Human Mobility

Cited by 308 publications

References 22 publications

Understanding predictability and exploration in human mobility

Understanding predictability and exploration in human mobility

Dynamic population mapping using mobile phone data

Toward green media delivery: location-aware opportunities and approaches

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