Destination choice game: A spatial interaction theory on human mobility

Yan, Xiao-Yong; Zhou, Tao

doi:10.1038/s41598-019-46026-w

Cited by 41 publications

(38 citation statements)

References 50 publications

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“…The gravity model is simple in form and has been successfully used to predict railway freight volume [22], subway passengers [23], highway traffic flow [24], air travel [25], commuting [26] and population migration [12]. Hereafter, researchers derived the gravity model from the perspective of destination selection behavior using the theory of determining utility [27], stochastic utility [28] and game theory [29]. Another classic model that is also established from the perspective of destination selection behavior is the intervening opportunity (IO) model [30].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

A universal opportunity model for human mobility

Liu

Yan

2020

Sci Rep

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“…In addition, studies on human dynamics have revealed some inherent regularities of human behaviors, such as memory and burstiness in temporal activities [317,318] and heterogeneity in mobility patterns [319,320], which remarkably influence single spreading dynamics. Once the human dynamics are included, the interaction patterns and transmissibility among nodes will be affected, and thus the coevolution spreading will also be affected.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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“…Ullah et al [27] also analyzed spatio-temporal data from LBSNs to show the impact of people in green spaces. Yan et al [28] investigated three different datasets, including Sina Weibo data, to analyse individual's decision-making regarding the places they tend to go and the influence of the economic aspects of crowds on hot spot destinations. They proved that the gravity model is well-suited to predicting the effects of mobility on destinations.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

Ebrahimpour

Wan

García

et al. 2020

IJGI

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Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

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Destination choice game: A spatial interaction theory on human mobility

Cited by 41 publications

References 50 publications

A universal opportunity model for human mobility

A universal opportunity model for human mobility

Coevolution spreading in complex networks

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

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