A universal model for mobility and migration patterns

By Filippo Simini, Marta C. González, Amos Maritan, Albert-László Barabási. 2011

1,537 citations recorded by the source corpus.

Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.

Subfields: Economics and Econometrics, Transportation

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, background): “Flows are typically described by an OriginDestination (OD) matrix, which has a specific structure and dynamics [170].”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “…, developing accurate PEV energy consumption models 28 , energy management 29,30 , smart charging strategies that probe centralized and decentralized approaches 31,32 , scheduling 33,34 , peak shaving, emissions, pricing models 35,36 and joint optimization of power and transportation networks 37 .”
Inferring international and internal migration patterns from Twitter data (2014, background): “More recently, the ‘radiation’ model, an approach that addresses some of the limitations of gravity-type models, has been suggested [21].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “Moreover, they analyzed several aspects of mobility patterns ranging from human trajectories (Song et al., 2010) to migration (Simini et al., 2012) and road usage patterns (Wang et al., 2012).”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, methodology): “returning to particular points, [36]), geographical environments [30,37–39], and distance effects [3,9,40].”
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, positive): “[9], were able to propose a universal model for human mobility based on observations in the census data and confirmed the model with mobile phone data.”
Human mobility: Models and applications (2017, background): “Furthermore, the model requires the estimation of a number of free parameters, making it rather sensitive to fluctuations or incompleteness in data [150, 42].”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, background): “Our selection mechanism gives a rank k to each alternative destination based on their distances to the trip origin (33 – 36).”
A Deep Gravity model for mobility flows generation (2020, background): “Although the gravity model has the clear advantage of being interpretable by design and of requiring a few parameters, it suffers from several drawbacks, such as the inability to accurately capture the structure of the real ﬂ ows and the greater variability of real ﬂ ows than expected 15,47,55 .”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “It has been proved that a simple stochastic process can capture local mobility decisions that help us derive analytically commuting and mobility fluxes, requiring as an input-only information on the distribution of population and facilities [41] without details about individual demographics, socioec”
Exploring human mobility with multi-source data at extremely large metropolitan scales (2014, background): “The Radiation model [14] infers the mobility based on the population density of an origin region and a destination region and that of the surrounding regions.”