Passenger Flow-Oriented Operating Period Division in Urban Rail Transit: A Hybrid SOM and K-Means Clustering Approach

The accurate division of operating periods in urban rail transit (URT) is crucial for reasonable scheduling. However, the current determination of operating breakpoints largely relies on the empirical judgment of operators, and symmetric period schemes are usually adopted, which fail to effectively reflect the uneven temporal distribution of passenger flow across different lines and directions. This study proposes a hybrid SOM–K-means framework for dividing daily operating periods based on automatic fare collection (AFC) data, the method extracts features from three dimensions of passenger flow, total volume, microscopic fluctuations and macroscopic distribution. A case study is conducted based on data from Tianjin URT Lines 1 and 2. The results demonstrate that the clustering-based operating period division effectively reveals transition periods between peak and off-peak hours, as well as late-night periods that are not captured by the existing scheme, while also reflecting temporal asymmetry across lines and directions. Consequently, compared to current schemes, this division offers a more accurate representation of passenger flow characteristics, enhancing the precision of scheduling work and operational efficiency. Moreover, the SOM–K-means method shows robust clustering performance and stability across various scenarios and sample sizes. This study offers insights for URT to achieve refined scheduling and demand-responsive operations based on passenger flow.