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A universal opportunity model for human mobility
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 gravitat...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070048/ https://www.ncbi.nlm.nih.gov/pubmed/32170196 http://dx.doi.org/10.1038/s41598-020-61613-y |
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author | Liu, Er-Jian Yan, Xiao-Yong |
author_facet | Liu, Er-Jian Yan, Xiao-Yong |
author_sort | Liu, Er-Jian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7070048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70700482020-03-22 A universal opportunity model for human mobility Liu, Er-Jian Yan, Xiao-Yong Sci Rep Article 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. Nature Publishing Group UK 2020-03-13 /pmc/articles/PMC7070048/ /pubmed/32170196 http://dx.doi.org/10.1038/s41598-020-61613-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liu, Er-Jian Yan, Xiao-Yong A universal opportunity model for human mobility |
title | A universal opportunity model for human mobility |
title_full | A universal opportunity model for human mobility |
title_fullStr | A universal opportunity model for human mobility |
title_full_unstemmed | A universal opportunity model for human mobility |
title_short | A universal opportunity model for human mobility |
title_sort | universal opportunity model for human mobility |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070048/ https://www.ncbi.nlm.nih.gov/pubmed/32170196 http://dx.doi.org/10.1038/s41598-020-61613-y |
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