Clustering the Stations of Bicycle Sharing System
Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade. Since the large number of bicycle stations distribute widely in the city, it is difficult to identify their unique attributes and characteristics directly. Oriented to the real bicycle hire da...
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| Published in | 东华大学学报（英文版） Vol. 33; no. 6; pp. 968 - 972 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2016
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| Subjects | |
| Online Access | Get full text |
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| Summary: | Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade. Since the large number of bicycle stations distribute widely in the city, it is difficult to identify their unique attributes and characteristics directly. Oriented to the real bicycle hire dataset in Hangzhou, China, the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly. Then, based on the spatial distribution and temporal attributes of calculated clusters, visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens. The experimental results demonstrate the relation between human mobility, the time of day, day of week and the station location. |
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| Bibliography: | 31-1920/N urban mining; human mobility analysis; visual analytics ; bicycle sharing system Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade. Since the large number of bicycle stations distribute widely in the city, it is difficult to identify their unique attributes and characteristics directly. Oriented to the real bicycle hire dataset in Hangzhou, China, the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly. Then, based on the spatial distribution and temporal attributes of calculated clusters, visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens. The experimental results demonstrate the relation between human mobility, the time of day, day of week and the station location. SHI Xiao-ying , YU Zhen-hai , XU Hai-tao , HUANG Bin-bin （ Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hang zhou 310018, China） |
| ISSN: | 1672-5220 |