摘要
城市轨道交通及其沿线土地一体化发展是城市可持续发展的关键问题。提高城市轨道交通可持续性和土地资源配置合理性的关键需掌握轨道交通客流与土地功能之间的依赖关系,而回归分析是研究二者关系的主要方法。既有研究对于土地利用的描述多基于用地面积等概略数据,难以揭示各类属性的用地对客流的影响机理及其空间效应。采用百度地图POI数据以刻画用地信息,提出城市轨道交通车站吸引范围内用地功能的细粒度描述方法,基于全局常参数和局部变参数的回归模型研究车站早高峰出站客流与粗细粒度土地利用的相互依赖关系及其空间效应。针对北京地铁的案例研究表明:车站出站客流与不同功能用地及POI的依赖程度和空间特征存在显著差异。早高峰出站客流更多地受到与就业通勤相关的商业服务业设施用地、公共管理与公共服务用地的影响。在细粒度层面上,出站客流对写字楼和政府机构的依赖性更大,二者显著分布在就业岗位密集的中心城区功能分区和城市核心区。基于细粒度POI的局部变参数模型能较好地识别各类土地利用对车站客流的影响及其空间异质性,案例研究表明车站客流与土地利用的依赖关系是各类属性功能用地影响及其空间效应的叠加。
The integrated development of urban rail transit and land use nearby is one of the most important issues for sustainable development of cities.To improve the sustainability of urban rail transit and the rationality of land resource allocation,it is of great importance to understand the dependence relationship between passenger flow of urban rail transit and functions of land use.Regression analysis is the main method to study this relationship.However,the descriptions of land use in existing research are mostly based on sketchy data such as land area,which is difficult to reveal the impact mechanism and spatial effects of land use of various attributes on passenger flow.To this end,this study utilizes Point of Interest(POI)data of Baidu Map to describe land use information,and proposes a fine-grained description method of land use function within the attraction scope of urban rail transit station.Based on the case of Beijing Subway,global regression models with constant parameters and local regression model with variable parameters are employed to study the dependence relationship and spatial effects of coarse and fine-grained land use with outbound passenger flow at morning peak.The case study of Beijing Subway shows that comprehensively considering the tradeoff between the explanatory power and complexity of models,and the effect of dealing with spatial dependence and heterogeneity,the geographically weighted regression(GWR)model with variable parameters has the best estimation compared with the global model with constant parameter.Its interpretation ability is 84%,and Moran’s I index of residuals is 0.0001,which can describe the spatial heterogeneity of the dependence of station outbound passenger flow and POI.The results also display that the Beijing’s urban rail transit station basically covers the social and economic center of the central city.These areas are usually developed in a high-intensity hybrid manner for land development.Moreover,the impact and spatial characteristics of land use with different attributes and functions on the morning peak outbound passenger flow are significantly different.For example,the morning peak outbound passenger flow is closely related with the land for commercial and business facilities,administration and public services,which are related to housing and employment,and the commuter between the two places.At the fine-grained level,the outbound passenger flow is more dependent on POI of office buildings and government agencies,which are significantly distributed in the central city functional areas and urban core areas with dense employment.The local model with variable parameters based on fine-grained POI can better identify the impact and spatial heterogeneity of various types of land use on station passenger flow.The case study indicates that the dependence of station passenger flow and land use is the superposition of impacts and spatial effects of various attribute functions of land use.
作者
彭诗尧
陈绍宽
许奇
牛家祺
PENG Shiyao;CHEN Shaokuan;XU Qi;NIU Jiaqi(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Ministry of Transport,Beijing Jiaotong University,Beijing 100044,China;Integrated Transportation Research Centre of China,Beijing Jiaotong University,Beijing 100044,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044.China)
出处
《地理学报》
EI
CSSCI
CSCD
北大核心
2021年第2期459-470,共12页
Acta Geographica Sinica
基金
中央高校基本科研业务费专项资金(2019JBM034)
国家自然科学基金项目(71621001,71890972/71890970)。
关键词
轨道交通
土地利用
客流
兴趣点
地理加权回归
urban rail transit
land use
passenger flow
Point of Interest(POI)
geographically weighted regression
