AI Safety for Control Rooms

A train stalls on a busy corridor. A freeway control room faces a weather-driven surge. On the wall, new AI summaries and alerts are lighting up—but no one is quite sure how much to trust them. As foundation models enter rail, bus, and traffic control centers, the issue is no longer whether to use AI, but how to do it without turning decision support into a new source of operational risk. Transit leaders are being asked to evaluate vendors, approve pilots, and sign off on deployments without a machine-learning background, while the consequences of a bad integration play out in live service. AI Safety for Control Rooms translates model-centric AI language into control-room terms: movements, headways, clearance, dwell, capacity, and incident response. Instead of abstract “alignment,” it focuses on failure modes that matter when you are moving people and vehicles in real time: hallucinated status, silently stale information, brittle edge cases, misleading confidence, and poorly designed fallbacks. Organized as a set of practical playbooks, the book shows how to frame acceptance criteria and test cases for AI features before procurement, probe vendor claims and reference architectures for operational robustness, design hybrid workflows where human operators remain in control, define UX patterns that fail safe under time pressure, and monitor AI components with SLOs, telemetry, and incident reviews. With chapters on sandboxing, simulation, synthetic data, and governance tailored to the U.S. transit context, it serves as a working manual for engineers, control-center managers, and systems integrators who need AI assistance that behaves predictably when the board lights up—and who must defend those choices to safety, legal, and the public.