Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China
Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China
复制标题
建成环境因素对车站层面轨道交通客流量的空间差异影响：以中国广州为例
DOI：
10.1016/j.jtrangeo.2019.102631
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发表时间：
2020-01-01
影响因子：
6.1
通讯作者：
Liu, Xiaoping
中科院分区：
文献类型：
--
作者：
Li, Shaoying;Lyu, Dijiang;Liu, Xiaoping
了解轨道交通乘客量与建成环境之间的关系对于促进以交通为导向的发展和可持续城市增长至关重要。地理加权回归（GWR）模型先前已被用来揭示这种关系的空间差异，在车站一级。然而，很少有研究的特点，在一个良好的规模建成环境，并将它们与轨道交通的使用。此外，现有的研究没有一个试图对车站进行分类，以考虑建筑环境的不同影响。在这项研究中，以广州为例，我们整合了多个空间大数据，如高空间分辨率的遥感图像，兴趣点（POI），社交媒体和建筑足迹数据，以精确量化建筑环境的特征。这是结合GWR模型，以了解如何在整个研究区域的轨道交通乘客的细尺度建成环境因素的影响。采用k-means聚类方法，根据当地台站的GWR模型系数识别不同的台站组。在此基础上提出了政策分区，并对不同分区提出了差异化的规划指导。这些建议预计将有助于提高轨道交通的使用率，为轨道交通规划提供信息（以减轻目前拥挤线路的交通负担），并重新分配工业和生活设施，以减少居民的通勤。政策和规划的影响是至关重要的协调发展的轨道交通系统和土地利用。
Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multisource spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.