Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City

Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the comm...

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Autores principales:	Chen Xie, Dexin Yu, Xiaoyu Zheng, Zhuorui Wang, Zhongtai Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c751023078ca4c7081d3798a4747df41
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spelling	oai:doaj.org-article:c751023078ca4c7081d3798a4747df412021-11-18T06:34:41ZRevealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City1932-6203https://doaj.org/article/c751023078ca4c7081d3798a4747df412021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577771/?tool=EBIhttps://doaj.org/toc/1932-6203Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the community structure shaped by travel, which is analyzed empirically in New York City. It uses spatial statistics and graph-based approaches to quantify travel behaviors and generate previously unobtainable insights. Specifically, people primarily travel for commuting on weekdays and entertainment on weekends. On weekdays, people tend to arrive in the financial and commercial areas in the morning, and the functions of zones arrived in the evening are more diversified. While on weekends, people are more likely to arrive at parks and department stores during the daytime and theaters at night. These hotspots show positive spatial autocorrelation at a significance level of p = 0.001. In addition, the travel flow at different peak times form relatively stable community structures, we find interesting phenomena through the complex network theory: 1) Every community has a very small number of taxi zones (TZs) with a large number of passengers, and the weighted degree of TZs in the community follows power-law distribution; 2) As the importance of TZs increases, their interaction intensity within the community gradually increases, or increases and then decreases. In other words, the formation of a community is determined by the key TZs with numerous traffic demands, but these TZs may have limited connection with the community in which they are located. The proposed analytical framework and results provide practical insights for urban and transportation planning.Chen XieDexin YuXiaoyu ZhengZhuorui WangZhongtai JiangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chen Xie Dexin Yu Xiaoyu Zheng Zhuorui Wang Zhongtai Jiang Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
description	Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the community structure shaped by travel, which is analyzed empirically in New York City. It uses spatial statistics and graph-based approaches to quantify travel behaviors and generate previously unobtainable insights. Specifically, people primarily travel for commuting on weekdays and entertainment on weekends. On weekdays, people tend to arrive in the financial and commercial areas in the morning, and the functions of zones arrived in the evening are more diversified. While on weekends, people are more likely to arrive at parks and department stores during the daytime and theaters at night. These hotspots show positive spatial autocorrelation at a significance level of p = 0.001. In addition, the travel flow at different peak times form relatively stable community structures, we find interesting phenomena through the complex network theory: 1) Every community has a very small number of taxi zones (TZs) with a large number of passengers, and the weighted degree of TZs in the community follows power-law distribution; 2) As the importance of TZs increases, their interaction intensity within the community gradually increases, or increases and then decreases. In other words, the formation of a community is determined by the key TZs with numerous traffic demands, but these TZs may have limited connection with the community in which they are located. The proposed analytical framework and results provide practical insights for urban and transportation planning.
format	article
author	Chen Xie Dexin Yu Xiaoyu Zheng Zhuorui Wang Zhongtai Jiang
author_facet	Chen Xie Dexin Yu Xiaoyu Zheng Zhuorui Wang Zhongtai Jiang
author_sort	Chen Xie
title	Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
title_short	Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
title_full	Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
title_fullStr	Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
title_full_unstemmed	Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
title_sort	revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: a case study of new york city
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c751023078ca4c7081d3798a4747df41
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Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City

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