Dwell time is a critical component of railway operations, influencing network capacity, service reliability, and passenger experience. Despite extensive methodological development, discussion of how dwell time models are operationalised in practice remains limited in the publicly available literature, a situation that may partly reflect commercial confidentiality in real-world applications. To address this, the paper proposes a novel lifecycle-oriented, systems-theoretic framework to support the selection, calibration, and operationalisation of dwell time models in alignment with institutional capabilities, data environments, and planning objectives. The framework is informed by a structured, non-exhaustive review of railway dwell time modelling approaches, synthesizing statistical-based, simulation-based, and advanced models to examine how passenger behaviour, operational constraints, and uncertainty are represented across different operational contexts. Unlike prior reviews that focus on individual modelling paradigms in isolation, this study integrates insights across major approaches and aligns them with practical deployment considerations. By introducing a six-part lifecycle framework, this work provides a structured, actionable pathway for translating dwell time models into real-world applications. By bridging academic rigor with real-world applicability, the proposed framework offers a pragmatic pathway for agencies to leverage data-driven modelling for improved dwell time management by advancing the operational maturity and responsiveness of railway systems. • Dwell time can critically affect rail punctuality, capacity, and passenger experience. • A gap exists between academic models and real-world application. • Proposes a conceptual six-stage lifecycle framework to guide practical model implementation. • Reviews statistical and simulation railway dwell-time models, focusing on passenger flow, rolling stock, and operations.
Ng et al. (Mon,) studied this question.