A Model-Based Characterisation of Delay Propagation in Metro Networks

Abstract

The reliability of a metro system is an important factor in building user trust and ridership, as it helps maintain efficient and sustainable urban mobility. Within the high-frequency environment of metro networks, even minor disturbances can propagate across the network, resulting in secondary delays that reduce schedule stability and passenger satisfaction. With the need for analysis approaches that balance predictive flexibility with analytical interpretability, this thesis develops a data-driven framework for modelling and quantifying delay propagation in metro networks.

The proposed framework reduces the modelling of delay propagation to the statistical fitting of relationship functions between network elements using subsets of available variable data. The methodology follows five general steps: defining relationship structures, setting analysis dimensions and their granularity, selecting the method for fitting the relationship functions, fitting the functions using available data, and quantifying the residual variability. This framework is applied to the Washington D.C. Metro using schedule and operational train movement data to analyse and characterise delay propagation behaviour.

Two model implementations are developed. The first explores the full breadth of variables available in the delay data to identify those offering statistically significant delay relationships, revealing that propagation occurs predominantly between directly connected stations and along shared lines. The second, more targeted model quantifies these relationships, demonstrating that propagation strength is independent of delay magnitude and mostly consistent across different time periods. Further cross-examination revealed minimal sensitivity to operational and network design variables, such as headways and connectivity, suggesting that other variables like scheduling regimes might be the cause of the different observed delay propagation behaviours.

The results highlight that localised delay effects dominate over network-wide influences, suggesting that metro operators can improve service reliability by focusing mitigation efforts on key inter-station connections rather than entire lines. The framework offers a versatile and multiplicative foundation for future applications in delay propagation prediction and analysis.