轨道站点合理步行可达范围建成环境与轨道通勤的关系研究——以北京市44个轨道站点为例

轨道站点步行可达范围内建成环境因素与轨道交通通勤行为之间的关系越来越受到人们的重视。从潜在通勤者的角度出发,划定轨道站点的合理步行可达范围,以北京市44个轨道站点为例,利用大数据方法从“宜出行”程序中提取站点合理步行范围内的相对人口数据,结合2017年9月10个工作日早高峰时段的轨道站点刷卡数据得到该站点的相对乘车率。基于数据的非正态分布特征构建GARCH模型,分析早高峰站点合理步行范围内建成环境因素与站点相对乘车率的关系。结果表明：① 始发轨道站点与站点乘车率存在显著正向关系,而站点所在线路的换乘概率与站点乘车率具有非常明显的负相关性;② 轨道站点的出入口数量与乘车率显著正相关;③ 小区—站点的路径转折数和步行范围内的交叉路口数等可步行性特征对轨道交通通勤行为无显著影响,步行范围的公交车站密度与站点乘车率正相关;④ 合理步行范围内的用地混合度与乘车率存在显著的负相关性;⑤ 合理步行范围内的路网密度以及早高峰拥堵道路比与乘车率之间在不同程度上呈现正向关系;⑥ 共享单车订单数与轨道交通通勤行为之间的关系并不明确;⑦ 相比手机信令数据,“宜出行”定位数据的精度更高,适用于分析微观尺度下的实时人口分布及变化。

Relationship between built environment of rational pedestrian catchment areas and URT commuting ridership: Evidence from 44 URT stations in Beijing

In recent years, there has been a growing interest in the relationship between built environment factors within station pedestrian catchment areas and urban rail transit (URT) commuting ridership. In this paper, the rational pedestrian catchment areas was studied from the perspective of potential commuters. Based on a case-study of 44 URT stations in Beijing, big data method was adopted to collect point data of population from 'Yichuxing', an internet application. In addition, relative values of relative riding rate were obtained by combining point data and rail transit one-card pass data during peak time within 10 working days in September 2017. In view of the abnormal distribution of data, a GARCH model was established to analyze the interactions between station relative riding rate and built environment factors within rational pedestrian catchment areas. The study results showed that (1) there is a notable positive correlation between URT relative riding rate and initial station, and negative interaction between station relative riding rate and transfer probability of station; (2) there is a strong positive relationship between relative riding rate and exit numbers of station; (3) there are no explicit relationships between conditions of station relative riding rate and walkable factors such as residential-station footpath turn times and cross numbers within rational catchment areas, whereas positive relationship was observed between station relative riding rate and bus stop density within rational pedestrian catchment areas; (4) significant negative correlation can be found between relative riding rate and land use mixture; (5) there are positive correlations among station relative riding rate and density of road network, congested road proportion in morning peak hours in varying degree; (6) there is an ambiguous and intricate relationship between bike-sharing order quantities and URT relative riding rate; (7) compared to cellular signaling data, "Yichuxing" point data showed higher accuracy and applicability in terms of the analysis of demographic distribution and micro-scale changes.