Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City
| Autor: | Zhuorui Wang, Dexin Yu, Chen Xie, Xiaoyu Zheng, Zhongtai Jiang |
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| Rok vydání: | 2021 |
| Předmět: |
Computer and Information Sciences
Economics Science Distribution (economics) Social Sciences Transportation Human Geography Civil Engineering Entertainment Transport engineering Urban Geography Leisure Activities Geoinformatics Land Use Spatial analysis Community Structure Geographic Areas Transportation planning Travel Multidisciplinary Ecology Geography business.industry Ecology and Environmental Sciences Community structure Biology and Life Sciences Outsourced Services Complex network Transportation Infrastructure Spatial Autocorrelation Roads Community Ecology Earth Sciences Medicine Human Mobility Engineering and Technology New York City business Automobiles Finance Research Article Urban Areas |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 11 (2021) PLoS ONE, Vol 16, Iss 11, p e0259694 (2021) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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