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| 基于地理加权回归的中心城区共享单车出行特征及影响因素研究 | |
| 引用本文： | 魏宗财,甄峰,莫海彤,刘晨瑜,彭丹丽. 基于地理加权回归的中心城区共享单车出行特征及影响因素研究[J]. 地理科学, 2020, 40(7): 1082-1091. DOI: 10.13249/j.cnki.sgs.2020.07.005 |
| 作者姓名： | 魏宗财 甄峰 莫海彤 刘晨瑜 彭丹丽 |
| 作者单位： | 1. 华南理工大学建筑学院，广东 广州 510641 2. 南京大学建筑与城市规划学院/江苏省智慧城市设计仿真与可视化技术工程实验室，江苏 南京 210093 |
| 基金项目： | 国家自然科学基金项目（41801150, 41571146）、广东省哲学社会科学规划项目（GD17YGL01）资助 |
| 摘 要： | 移动信息通讯技术的发展和全面渗透融入社会生活，导致高度压缩的时间、空间和距离，重塑了城市居民行为活动模式。共享单车作为“互联网+共享”的新型出行方式改善了居民的出行方式。但既有成果对共享单车的研究尚不充分。使用摩拜单车1周的骑行数据，剖析广州中心城区共享单车出行轨迹的时空间分布特征，基于地理加权回归方法进一步探究建成环境的功能密度因素对共享单车出行的影响及程度。研究发现，共享单车在工作日和休息日的出行均具有明显的早晚高峰特征。公共交通站点POI密度、功能混合度、机动车道密度等因素的边际作用表现出显著的空间不稳定性。研究能为共享单车企业提升运营水平和政府优化慢行交通环境提供参考。 |
| 关 键 词： | 共享单车 出行特征 POI数据 广州 地理加权回归 |
| 收稿时间： | 2019-07-23 |
Travel Characteristics and Influencing Factors of Sharing Bicycles in Central Urban Areas Based on Geographically Weighted Regression: The Case of Guangzhou City |
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| Wei Zongcai,Zhen Feng,Mo Haitong,Liu Chenyu,Peng Danli. Travel Characteristics and Influencing Factors of Sharing Bicycles in Central Urban Areas Based on Geographically Weighted Regression: The Case of Guangzhou City[J]. Scientia Geographica Sinica, 2020, 40(7): 1082-1091. DOI: 10.13249/j.cnki.sgs.2020.07.005 | |
| Authors: | Wei Zongcai Zhen Feng Mo Haitong Liu Chenyu Peng Danli |
| Affiliation: | 1. School of Architecture, South China University of Technology, Guangzhou 510641, Guangdong, China 2. School of Architecture and Urban Planning, Nanjing University/Jiangsu Provincial Engineering Laboratory of Smart City Design Simulation & Visualization, Nanjing 210093, Jiangsu, China |
| Abstract: | With the skyrocketing development of mobile information and communication technology and its penetration into everyday life, time, space and distance have been highly compressed. Spatiotemporal constraints of human behavior have been reduced significantly. As a result, the relationship between residents, time and space has been reconstructed, which further reshapes the pattern of urban residents’ behavior. As a new-type travel mode of ‘Internet + sharing’, sharing bicycle provides a more convenient and diversified choice for urban residents’ daily travel. The extant studies mainly focus on the travel characteristics and modes of public bicycle and their influencing factors, while sharing bicycle has been less touched. This article investigates the spatiotemporal distribution characteristics of sharing bicycle travel trajectories in the central urban area of Guangzhou by using one-week Mobike travel data. Then, this study further explores the impacts of functional density factors of built environment on the travel behaviors of sharing bicycles and to what extent, based on geographically weighted regression method. It has been found that the travel behaviors of Mobike show obvious morning and evening peak hours on both weekdays and weekends, and they are not significantly affected by the thunder showers. On weekdays, the new urban area with more employment opportunities, such as Zhujiang New Town and Tianhe Software Park, has shown higher travel density of sharing bicycles, while on the weekends, the travel density in this urban area has significantly reduced. Furthermore, compared with the morning peak, the travel behaviors at the late peak are more concentrated in the core area, while less in the marginal area. The old urban areas with the characteristics of highly functional mix, dense road network and bicycle-friendly become the main travel areas of sharing bicycles. Among them, the density of public transportation station, functional mixing degree and the density of motor vehicle lane show strongly significant influences on the travel behaviors of sharing bicycles. Moreover, improving the quality of the above-mentioned factors can largely promote the travel behaviors of sharing bicycles in most of research areas. This study can provide references for bike-sharing enterprises to improve their operation and management, the government to enhance the slow traffic environment and the quality of citizens’ travel. |
| Keywords: | sharing bicycles travel characteristics POI data Guangzhou geographically weighted regression |
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