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Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic di...

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Autores principales: Kung, Kevin S., Greco, Kael, Sobolevsky, Stanislav, Ratti, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059629/
https://www.ncbi.nlm.nih.gov/pubmed/24933264
http://dx.doi.org/10.1371/journal.pone.0096180
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author Kung, Kevin S.
Greco, Kael
Sobolevsky, Stanislav
Ratti, Carlo
author_facet Kung, Kevin S.
Greco, Kael
Sobolevsky, Stanislav
Ratti, Carlo
author_sort Kung, Kevin S.
collection PubMed
description Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.
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spelling pubmed-40596292014-06-19 Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data Kung, Kevin S. Greco, Kael Sobolevsky, Stanislav Ratti, Carlo PLoS One Research Article Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors. Public Library of Science 2014-06-16 /pmc/articles/PMC4059629/ /pubmed/24933264 http://dx.doi.org/10.1371/journal.pone.0096180 Text en © 2014 Kung et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kung, Kevin S.
Greco, Kael
Sobolevsky, Stanislav
Ratti, Carlo
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title_full Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title_fullStr Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title_full_unstemmed Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title_short Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
title_sort exploring universal patterns in human home-work commuting from mobile phone data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059629/
https://www.ncbi.nlm.nih.gov/pubmed/24933264
http://dx.doi.org/10.1371/journal.pone.0096180
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