Spatiotemporal Mode Analysis of Urban Dockless Shared Bikes based on Point of Interests Clustering

doi:10.16182/j.issn1004731x.joss.19-FZ0327

Abstract

Abstract: The city’s dockless shared bikes have developed rapidly, and its features of convenience, economy and efficiency have been widely welcomed. The digital footprint they generate reveals the movement of people in time and space within the city, which makes it possible to quantify the activities of people in the city using shared bikes. In this paper, based on the collected shared bikes data of Beijing, a clustering method based on the point of interests is proposed to divide the urban space, so as to construct a mobile network of urban shared bikes, and analysis the spatiotemporal mode of bike flow from different perspectives. The research in this paper is helpful to understand the characteristics of urban residents' travel and help urban managers to design and plan the urban traffic management systems.

Key words: dockless shared bikes, point of interests, clustering, mobile network, spatiotemporal mode

CLC Number:

TP391.9

Zhang Fang, Chen Bin, Tang Yanghua, Dong Jian, Ai Chuan, Qiu Xiaogang. Spatiotemporal Mode Analysis of Urban Dockless Shared Bikes based on Point of Interests Clustering[J]. Journal of System Simulation, 2019, 31(12): 2829-2836.

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