Evaluating Statistical Models of Railway Dwell Time: Video-Based Evidence from Regional Railways in Victoria, Australia

Accurate prediction and management of train dwell times are essential for achieving efficient and sustainable public transport operations. This study evaluates established statistical dwell-time models within the context of Victoria’s regional railway network, contrasting their predictions with empirical data from video-based observations. Historically, these models—rooted in linear and non-linear regression analyses—have been designed for urban settings in peak periods. However, their applicability to regional railways, characterised by lower service frequencies with unique infrastructure and operational constraints, has been underexplored. The models were assessed for their ability to predict both passenger flow time and total dwell time under regional operating conditions. Results show that while passenger flow time can be predicted with moderate accuracy (best model R 2 ≈ 0.65), total dwell time models perform considerably worse (best model R 2 ≈ 0.25), largely due to unmodelled operational delays. The analysis identifies door operation cycles and conductor procedures as the primary operational variables influencing variability in total dwell time. Additionally, variations in passenger behaviour between peak and off-peak periods affect model performance. The findings underscore the need to incorporate local operational and behavioural factors into dwell-time models to enhance their predictive reliability for regional rail contexts. This study provides an empirical foundation for refining dwell time modelling approaches, supporting policymakers and operators in improving scheduling efficiency and overall service sustainability in regional rail networks.