Understanding the Spatial Alignment Between Public Rental Housing and Metro: Evidence from Zhengzhou, China

The population for each PRH community was estimated by multiplying the number of households by the citywide average household size of 3.46 persons per household, obtained from Zhengzhou's Seventh National Population Census. While we acknowledge the limitation of not having household-level census data for PRH, our field survey provided ancillary support for the population estimation framework. Through informal conversations with property management staff at 22 of the surveyed communities, we confirmed that the reported number of households for each community was consistent with management records. This at least verifies the reliability of the 'household count' component of our population estimation.Metro network and passenger flow data. Data on metro lines and station locations were sourced from the AMap application programming interface for October 2024, ensuring alignment with the operational network during the study period. Passenger flow data was provided by the Zhengzhou Metro Corporation and consists of anonymized smart card transaction records from September 1 to 30, 2024. These records contain card IDs, entry and exit station names, and timestamps. From this, we extracted the inbound passenger flow for each station during the weekday morning peak period (7:00–9:00 AM) and calculated the average daily inbound flow for this period for each station. This passenger flow data is critically used in our model to quantify station-level congestion and its impact on waiting time.Transportation network data. The road network data for Zhengzhou was sourced from the 2023 version of the electronic map database. It includes vector data for primary roads, secondary roads, branch roads, and inter-urban highways. This dataset provides the fundamental network structure for conceptualizing travel paths.

各PRH社区（PRH community）的人口规模通过将家庭户数乘以全市平均家庭户规模（3.46人/户）估算得到，该平均规模取自郑州市第七次全国人口普查结果。尽管我们承认，由于缺乏针对PRH社区的家庭级普查数据，该估算方法存在一定局限性，但我们的实地调研为人口估算框架提供了辅助支撑。通过与22个受访社区的物业管理人员开展非正式访谈，我们证实各社区上报的家庭户数与物业留存记录相符，这至少验证了本次人口估算中“家庭户数统计”模块的可靠性。
地铁网络与客流数据。地铁线路及站点位置数据取自2024年10月的高德地图（AMap）应用程序编程接口，确保与研究时段内的运营地铁网络保持一致。客流数据由郑州地铁集团有限公司提供，为2024年9月1日至30日的匿名智能卡交易记录，包含卡号、进出站名称及时间戳信息。我们从中提取了各站点工作日早高峰时段（7:00–9:00）的进站客流，并计算得到该时段各站点的日均进站客流量。该客流数据是本研究模型中量化站点级拥堵及其对候车时间影响的核心依据。
道路交通网络数据。郑州市道路交通网络数据取自2023版电子地图数据库，包含主干道、次干道、支路及城际高速公路的矢量数据，该数据集为构建出行路径的概念化模型提供了基础网络结构。