Embedding automated train operations safety within urban mobility transitions: the Sydney metro case in smart city rail transformation

Document Type

Journal Article

Publication Date

2026

Subject Area

place - australasia, place - urban, mode - rail, mode - subway/metro, planning - methods, planning - safety/accidents, technology - intelligent transport systems

Keywords

Predictive analytic methods (PAM), automatic train operation (ATO), grade of automation 4 (GoA4), intelligent transportation systems (ITS), modern safety system deployment

Abstract

Automated Train Operations (ATO) are increasingly central to smart city mobility strategies, yet their safety implications remain underexplored from a systems planning perspective. This study reframes ATO safety as a question of urban mobility resilience and infrastructure transition, examining how integrated modelling can support evidence-based planning for next-generation rail systems. Using Sydney Metro’s Northwest corridor as an empirical case, the research develops a hybrid Fuzzy-Markov and cross-sectional systems modelling approach to evaluate safety performance across staged operational efficiency thresholds (<70%, <80%, <90%). Simulation results indicate that lower-efficiency transition scenarios converge more rapidly towards optimal safety performance, while near-optimal systems exhibit slower stabilisation dynamics. These findings reveal nonlinear safety transition patterns that are critical for infrastructure phasing, risk governance, and investment sequencing in automated rail environments. The analysis further identifies five system-level challenges embedded within the corridor’s eight-station Intelligent Transportation Systems (ITS) architecture, highlighting the interaction between digital infrastructure, operational integration, and urban-scale risk management. Results demonstrate that the ITS-enabled ATO regime delivers measurably stronger system resilience and safety convergence compared with legacy operations. Beyond technical validation, the study provides urban planners and transport authorities with a decision-support model capable of visualising safety trajectories during major technological transitions. The paper contributes to urban mobility scholarship by integrating probabilistic modelling with cross-sectional systems analytics to support strategic planning for automated rail in smart cities.

Rights

Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.

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