First map the entire system in a spreadsheet, then split it into logical modules—each one can be deployed, tested, and rolled back independently. Start with a small, low‑traffic pilot in a controlled area so you can observe all interactions without risking the whole network. Use simulations to stress‑test peak‑load scenarios before the pilot. Once the pilot proves stable, add one new module at a time, running a full acceptance test after each addition. Keep a rollback plan on hand for every module; if something fails, you revert only that module, not the whole hub. Throughout, maintain a risk register, assign owners to each risk, and review it weekly with key stakeholders. Finally, schedule a debrief after each phase to capture lessons learned and update the plan before moving to the next module.

Got it. I’ll split the risk register into three buckets—technical, operational, external—each with defined severity levels so we know when to act. For the pilot, I’ll pick a corridor that has historically seen peak surges; that way we’ll test the system under a sudden commuter spike. I’ll set up a live dashboard that watches key metrics and triggers an automated rollback if any threshold is crossed. That keeps the rollout controlled and lets us tighten any slack before the full launch.

The dashboard will track throughput, passenger wait times, error counts, and UI responsiveness; each metric will have a threshold and an automated alert. For the rollback, I’ll define a two‑step fail‑over: first switch traffic to the previous stable module, then re‑initiate the new module only after a verification pass. I’ll schedule hourly checks during rollout and weekly reviews thereafter to keep the cadence tight and the system ahead of issues.

Add a lightweight health‑probe that runs in seconds before the rollback re‑starts the new module; if it fails, we stay on the old version until the probe clears. Keep the hourly checks to a single dashboard view and only flag critical alerts—this will keep the team focused and avoid alarm fatigue.

I’ll expand the health‑probe to include user‑journey flows, database latency, queue depth, and UI render times, not just API endpoints. Each check will return a pass/fail code and a severity level; the dashboard will aggregate these into a single health score. Alerts will be threshold‑based and include a recommended next step, so the ops team can act immediately without sifting through logs. That keeps the focus tight while still covering every critical path.