地球信息科学学报

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (1): 25-35.doi: 10.12082/dqxxkx.2019.180199

• 地理大数据时空模式挖掘的方法与应用研究 • 上一篇    下一篇

中小城市公共自行车出行模式与驱动机制研究

王陆一1,5(), 吴健生1,2,*(), 李卫锋3,4   

  1. 1. 北京大学城市规划与设计学院,城市人居环境科学与技术重点实验室,深圳518055
    2. 北京大学城市与环境学院,地表过程与模拟教育部重点实验室,北京100871
    3. 香港大学建筑学院城市规划及设计系,香港 999077
    4. 香港大学深圳研究院,深圳 518057
    5. 滴滴出行地图事业部,北京 100094
  • 收稿日期:2018-04-23 修回日期:2018-09-23 出版日期:2019-01-20 发布日期:2019-01-20
  • 作者简介:

    作者简介：王陆一（1993-）,男,硕士生,研究方向为城市与区域规划,时空数据分析与挖掘。E-mail:wangluyi@pku.edu.cn

  • 基金资助:
    国家自然科学基金面上项目（41471370）.

Usage Patterns and Driving Mechanisms of Public Bicycle Systems in Small and Medium-Sized Cities based on Space-Time Data Mining

Luyi WANG1,5(), Jiansheng WU1,2,*(), Weifeng LI3,4   

  1. 1. Key Laboratory for Urban Habitant Environment Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen, Guangdong 518055, China
    2. Laboratory for Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    3. Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hongkong 999077, China
    4. Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, China
    5. Map Division, Didi Chuxing, Beijing 100094, China
  • Received:2018-04-23 Revised:2018-09-23 Online:2019-01-20 Published:2019-01-20
  • Contact: Jiansheng WU
  • Supported by:
    National Natural Science Foundation of China, No.41471370

RichHTML

PDF (PC)

赞

被引次数

2

摘要：

公共自行车在促进交通可持续发展、方便居民出行等方面意义重大。本文以广东省惠州市惠城区和惠阳区、韶关市区公共自行车为研究对象,利用时间序列数据分析、层次聚类、地理可视化等方法探究出行模式,利用随机森林算法分析出行行为的影响因素及其重要性程度。研究发现,惠州市惠城区和韶关市的公共自行车出行行为有规律、成规模,站点呈现生活居住、工作就业等类型;惠州市惠城区自行车使用目的多元,包括通勤、短途办事、公交接驳等,韶关市中,通勤是主要出行目的。惠州市惠阳区受到骑行道路限制,公共自行车使用率低。本研究可以为提高公共自行车系统运营效率、引导慢行交通政策制定、评估城市用地布局提供参考和建议,也能为其他区域公共自行车的研究提供借鉴作用。

关键词: 公共自行车, 中小城市, 时空数据挖掘, 随机森林, 驱动机制

Abstract:

Given the importance of environment-friendly cities, the development of public bicycle systems (PBSs) has become more popular in recent years around the world. The purpose of this study was to explore the usage patterns of PBSs in small and medium-sized cities in Guangdong province, China, and to infer the driving mechanisms of system attributes and the built environment. The research applied time series analysis of global activity patterns, hierarchical clustering algorithm using Dynamic Time Warping distances as features and spatial data visualization on station-based data, and then compared different systems by employing a random forest algorithm to evaluate the influencing factors. The study objective was to better understand the relationship between public bicycle usage activity and underlying built environment characteristics. In Huicheng District of Huizhou City and Shaoguan City, the public bicycle usage patterns are regular, and bicycle stations are grouped into several clusters based on usage patterns of "morning destination, night origin" "morning origin, night destination" and "steady throughout the day". The PBS in Huicheng District plays various roles by helping users commute to and from jobs and schools, and to make short distance trips. The PBS also is a complementary tool for bus transit facilities. The PBS in Shaoguan City mostly serves as a mode for commuting. The PBS is inefficiently used in Huiyang District of Huizhou City owing to the poor road conditions. This research provides a study framework that can be reproduced in other areas, and offers a way of optimizing PBSs, thereby assisting urban transportation planning and urban land use allocation.

Key words: public bicycle, small and medium-sized cities, spatio-temporal data mining, random forest, driving mechanisms