Recognizing Social Function of Urban Regions by Using Data of Public Bicycle Systems

作     者：XU Haitao YING Jing XU Haitao;YING Jing

作者机构：College of Computer Science and TechnologyZhejiang University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2019年第28卷第1期

页      面：13-20页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：supported by the National Natural Science Foundation of China(No.61572165) the Public Projects of Zhejiang Province(No.LGF18F030006) 

主　　题：Urban regions recognition Public bicycle system Social function classification Smooth support vector machine(SSVM) 

摘      要：Obtaining the classification of urban functions is an integral part of urban planning. Currently,public bicycle systems are booming in these years. It conveys human mobility and activity information, which can be closely related to the social function of an urban region. This paper discusses the potential use of public bicycle systems for recognizing the social function of urban regions by using one year’s rent/return data of public bicycles. We found that rent/return dynamics, extracted from public bicycle systems, exhibited clear patterns corresponding to the urban function classes of these regions. With seven features designed to characterize the rent/return pattern, our method based on Smooth support vector machine(SSVM) is proposed to recognize social function classes of urban regions. We evaluate our method based on the large-scale real-world dataset collected from the public bicycle system of Hangzhou. The results show that our method can efficiently recognize different types of urban function areas. Classification results using the proposed SSVM achieved the best classification accuracy of 96.15%.