Research on spatial and temporal characteristics of taxi based on improved K-means with entropy weight: a perspective combined with urban functional area

Zhichun Jiang; Juan Li; Junfeng Wu

doi:10.1117/12.2642644

28 July 2022 Research on spatial and temporal characteristics of taxi based on improved K-means with entropy weight: a perspective combined with urban functional area

Zhichun Jiang, Juan Li, Junfeng Wu

Author Affiliations +

Proceedings Volume 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022); 123031W (2022) https://doi.org/10.1117/12.2642644
Event: International Conference on Cloud Computing, Internet of Things, and Computer Applications, 2022, Luoyang, China

Abstract

This paper takes 265 districts divided by New York City as the study unit and uses one month's taxi operation data of New York City to analyze the taxi travel mode and urban functional pattern of New York residents. In the orbit data analysis, the travel time series, order prices and travel volume of each block are extracted from the orbit data, and the travel volume time series data are clustered using the spatio-temporal clustering method to study the spatio-temporal distribution characteristics of passenger travel. Finally, the POI data of New York City is combined. This paper explores the travel rules of different cluster areas and summarizes the urban functional division of each cluster area. The results show that the time series model of taxi travel has significant differences between weekdays and weekends. In addition, weekdays show two peak travel peaks in the morning and evening, which roughly correspond to the commuting peak, and the number of weekend night trips is greatly increased. Quantitative analysis of the obtained regional clusters shows that the differences among the clusters are significant and conform to the actual urban regional functional structure. The results of this study have some significance for the analysis of taxi travel profile and taxi operation management in New York City

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Zhichun Jiang, Juan Li, and Junfeng Wu "Research on spatial and temporal characteristics of taxi based on improved K-means with entropy weight: a perspective combined with urban functional area", Proc. SPIE 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), 123031W (28 July 2022); https://doi.org/10.1117/12.2642644

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KEYWORDS

Global Positioning System

Statistical analysis

Analytical research

Data modeling

Visualization

Data analysis

Data processing

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