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Impact of built environment on residential online car-hailing trips: Based on MGWR model

With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using...

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Detalles Bibliográficos
Autores principales: Cao, Yan, Tian, Yongzhong, Tian, Jinglian, Liu, Kangning, Wang, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671434/
https://www.ncbi.nlm.nih.gov/pubmed/36395284
http://dx.doi.org/10.1371/journal.pone.0277776
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author Cao, Yan
Tian, Yongzhong
Tian, Jinglian
Liu, Kangning
Wang, Yang
author_facet Cao, Yan
Tian, Yongzhong
Tian, Jinglian
Liu, Kangning
Wang, Yang
author_sort Cao, Yan
collection PubMed
description With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.
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spelling pubmed-96714342022-11-18 Impact of built environment on residential online car-hailing trips: Based on MGWR model Cao, Yan Tian, Yongzhong Tian, Jinglian Liu, Kangning Wang, Yang PLoS One Research Article With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction. Public Library of Science 2022-11-17 /pmc/articles/PMC9671434/ /pubmed/36395284 http://dx.doi.org/10.1371/journal.pone.0277776 Text en © 2022 Cao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cao, Yan
Tian, Yongzhong
Tian, Jinglian
Liu, Kangning
Wang, Yang
Impact of built environment on residential online car-hailing trips: Based on MGWR model
title Impact of built environment on residential online car-hailing trips: Based on MGWR model
title_full Impact of built environment on residential online car-hailing trips: Based on MGWR model
title_fullStr Impact of built environment on residential online car-hailing trips: Based on MGWR model
title_full_unstemmed Impact of built environment on residential online car-hailing trips: Based on MGWR model
title_short Impact of built environment on residential online car-hailing trips: Based on MGWR model
title_sort impact of built environment on residential online car-hailing trips: based on mgwr model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671434/
https://www.ncbi.nlm.nih.gov/pubmed/36395284
http://dx.doi.org/10.1371/journal.pone.0277776
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