A Data-Driven Approach for Estimating the Effects of Station Closures in Metro Systems

Abstract

To estimate the impact of station closures, a novel data-driven approach is developed. First, Symbolic Aggregate Approximation (SAX) is designed to classify stations with an anomaly in passenger flow volume. Secondly, to define the trend anomaly within each segment, the Dynamic Time Warping (DTW) approach is suggested. The method simultaneously considers the mean value and trend information for passenger flow data. To check the efficiency of the proposed model, a case analysis of the Beijing metro system is illustrated. The findings suggest that the proposed model is superior in terms of the calculation of the impact of station closures to other state-of-the-art models.