Analyzing Train Delay Impacts on Subway Stations via a Three-Stage Approach: An Empirical Study on Shanghai and Shenzhen Metro Systems

Transit delays can adversely affect passengers, operational efficiency, and daily lives. It is important to develop effective methods to identify and analyze train stations vulnerable to delays. This paper proposes a three-stage analytical framework for analyzing train station delays. In the first stage, the 3-sigma rule defines normal passenger volume ranges and establishes a time window affected by delays. Next, a multivariate time series clustering method identifies stations with stable demand and high volume, considering passenger volume differences both among and within stations. In the final stage, the effects of delays on these key stations are assessed by examining starting, duration, and ending times, and passenger volume variation, providing a comprehensive analysis of delay impact. The proposed framework is illustrated using two real-world incidents: the 2021 delay incident at Longyang Road Station of Shanghai Metro and the 2019 delay incident on the Taoyuan–Luohu section of Shenzhen Metro. Case studies revealed that affected stations are not limited to the specific line or direction of the delay, but also include opposite-direction and transfer stations. Station impacts exhibit phased onset and recovery patterns. Additionally, both increases and decreases in passenger volumes due to the delay present considerable implications. While both incidents exhibit common propagation and recovery patterns, the Shanghai incident displays wider passenger impacts and longer recovery periods, whereas the Shenzhen incident exhibits narrower impacts and faster recovery. Our results will aid transit managers in better managing delays, thereby improving passenger satisfaction and operational efficiency. This paper also offers an integrated station-level analytical framework and initial cross-case empirical evidence, while broader validation remains needed.