为识别城市轨道交通网络关键站点并研究其多年演化，构建基于截断奇异值分解(truncated singular value decomposition，TSVD)的关键站点识别方法，选取北京市2011—2019 年早高峰时段的OD 数据，通过关键特征向 量分析网络客流演变并对城轨网络中关键站点进行识别，将其与复杂网络方法的识别结果进行对比。分析表明： TSVD 法能很好地应用于考虑OD 分布的网络关键站点识别，识别结果能更好代表网络客流的空间分布。从识别 结果看，北京轨道交通关键站点空间布局呈现多中心发展趋势，如西北西二旗，西南丰台科技园等站点逐步形成 网络客流中心并相互联系；东南土桥、东北俸伯等站点也初步呈现网络客流中心的特征。

To identify key stations in urban rail transit networks and study their evolution over multiple years, we developed a key station recognition method based on truncated singular value decomposition (TSVD). We selected origin-destination (OD) data from the morning peak hours between 2011 and 2019 in Beijing as the dataset. The analysis involved evaluating the evolution of network passenger flow and identifying key stations in subway networks using key eigenvectors. These results were then compared with those obtained from complex network methods. The analysis demonstrates that TSVD can effectively identify key network stations by considering the OD distribution. The outcomes from TSVD better represent the spatial distribution of network passenger flow than traditional methods. The results revealed that the spatial layout of key stations in Beijing’s urban rail transit has evolved towards multiple centers, such as Northwest Xi’erqi and Southwest Fengtai Science Park. These stations are gradually forming network passenger flow centers and establishing connections. Additionally, stations like Southeast Tu Qiao and Northeast Fengbo are showing preliminary trends toward becoming network passenger flow centers

U231

国家级大学生创新创业训练计划资助项目(202410004187)