In this thesis, automated fare collection (AFC) data are used to analyse and quantify transit journey time service quality on the London Underground metro system. The thesis comprises of three main research areas. The first part focuses on characterising passenger journey time variance through the generation of empirical probability distributions of journey times under regular and incident-affected operating conditions. The distributions are parametrically defined, and practical passenger-oriented performance metrics are proposed based on the moments of the distributions. The second area of research involves decomposing total passenger journey times recorded by the AFC data into sub-components that distinguish between the walking and in-vehicle phases of a passenger journey. To achieve this, a Bayesian assignment algorithm is proposed to allocate individual passengers to individual trains. Total journey times are then decomposed into the constituent components of access, on-train, and egress times. In the third area of research, the degree to which different service supply and demand factors influence journey times is analysed. Semiparametric regression methods are applied to quantify the effect of physical station and route characteristics, operational service supply factors, and passenger demand levels for each journey time component. To quantify the effect of individual passenger characteristics on journey times, passenger-level heterogeneity within each journey time component is analysed. As an extension to the access time model, the influence of train headways on passenger wait times at the origin station is also derived.

The main outputs of the thesis are the quantification of journey time performance, and the identification of the key service supply and demand factors that impact journey times. The results can be directly applied by operators to guide where potential interventions should be made in order to improve the reliability of journey times for urban rail transit networks.