A stochastic model of randomly accelerated walkers for human mobility

Gallotti, Riccardo; Bazzani, Armando; Rambaldi, Sandro; Barthélemy, Marc

doi:10.1038/ncomms12600

Cited by 96 publications

(100 citation statements)

References 61 publications

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“…Despite its fine performance in predicting human mobility, the UO model has room for further improvements. For example, most existing IO class models use an agent to represent all of the individuals and neglect the diversity of individual selection behavior [46,[55][56][57][58][59]. Building mobility prediction model for each individual may reflect the diversity in detail.…”

Section: Discussionmentioning

confidence: 99%

A universal opportunity model for human mobility

Liu

Yan

2020

Sci Rep

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Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.

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Section: Discussionmentioning

confidence: 99%

A universal opportunity model for human mobility

Liu

Yan

2020

Sci Rep

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“…Yet the phone sector is too fragmented to cover a global scale since the data is usually restricted to a single country, making crossborder movements hard to observe. Other examples with similar, yet even more reduced geographical scope, are the GPS tracks left by cars [21].…”

Section: Introductionmentioning

confidence: 99%

Migrant mobility flows characterized with digital data

Mazzoli

Diechtiareff²,

Tugores

et al. 2020

PLoS ONE

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Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground.

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“…and human health research [10]. With the rapid development of information and communication technology [11] in the past two decades, various types of massive digital footprints generated by humans such as smart card data, call detail records (CDRs), geo-tagged social media data, GPS tracking data, WiFi data, credit-card records data, and their concomitant analytics are used for human mobility research [2,[12][13][14][15][16][17][18]. However, there is debate regarding the representativeness or inherent biases of the data.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

Fang

Zhang

et al. 2017

IJGI

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Abstract:The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual's records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2-to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.

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A stochastic model of randomly accelerated walkers for human mobility

Cited by 96 publications

References 61 publications

A universal opportunity model for human mobility

A universal opportunity model for human mobility

Migrant mobility flows characterized with digital data

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

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