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A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering

1
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2
The School of Communication and Design, Sun yat-sen University, Guangzhou 510000, China
3
Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion, Guangzhou 510006, China
4
Scientific Data Center, Computer Network Information Center, Chinese Academy of Sciences, 4th South Fourth Road Zhongguancun, Beijing 100190, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 345; https://doi.org/10.3390/ijgi8080345
Received: 11 June 2019 / Revised: 25 July 2019 / Accepted: 29 July 2019 / Published: 31 July 2019
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Abstract

With the advantages of convenient access and free parking, urban dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for the research of flow pattern. In this paper, the flow characteristics of dockless shared bikes are expounded through the analysis of the time series location data of ofo and mobike shared bikes in Beijing. Based on the analysis, a model called DestiFlow is proposed to describe the spatio-temporal flow of urban dockless shared bikes based on points of interest (POIs) clustering. The results show that the DestiFlow model can find the aggregation areas of dockless shared bikes and describe the structural characteristics of the flow network. Our model can not only predict the demand for dockless shared bikes, but also help to grasp the mobility characteristics of citizens and improve the urban traffic management system. View Full-Text
Keywords: dockless shared bike; points of interest; aggregation area; spatio-temporal flow model dockless shared bike; points of interest; aggregation area; spatio-temporal flow model
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Dong, J.; Chen, B.; He, L.; Ai, C.; Zhang, F.; Guo, D.; Qiu, X. A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering. ISPRS Int. J. Geo-Inf. 2019, 8, 345.

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