D2V Mock
Transit
Design a dispatcher-controlled disruption-recovery service that improves network performance without hiding accessibility, labor, safety, critical-destination, or minimum-service tradeoffs.
Which recovery options remain safe and defensible when the network is already disrupted?
The simulated regional transit operator manages bus failures, operator shortages, road closures, weather, major events, communications loss, and passenger-impact signals across disconnected systems. Leadership asks for automatic resource reallocation to improve on-time performance.
The FDE challenge is to turn that request into an inspectable decision service. The deployment must identify feasible options, protect accessibility and minimum service, represent passenger consequence and uncertainty, and preserve dispatcher and operating authority.
One operating decision. Multiple deployment boundaries.
The Core Labs isolate individual failure modes. This mission forces several of them to coexist under one customer timeline, one evidence room, and one final deployment recommendation.
Can disruption-recovery recommendations improve network performance without violating accessibility, labor, safety, service-floor, or dispatcher authority constraints?
Passenger counts, route criticality, vehicle readiness, operator eligibility, accessibility needs, and incident data arrive with different freshness and consequence.
Release, constrain, or redesign dispatcher decision support with hard constraints, explicit human authority, and a review surface that preserves decision-changing evidence.
6 Core Labs are exercised here
Network speed is only one definition of success.
The mission requires explicit authority and tradeoffs across the control center, fleet, workforce, accessibility, service planning, safety, communications, and riders.
Control-center supervisor
Needs fast, feasible options and a stable operating picture.
Fleet and workforce control
Own vehicle readiness, qualifications, hours, and resource feasibility.
Accessibility and rider impact
Protects accessible service, practical alternatives, and riders poorly represented by averages.
Service planning and communications
Owns minimum service floors, critical destinations, and accurate public commitments.
Passenger consequence and resource truth must be reconstructed.
The evidence room combines operating briefs, interviews, procedures, incident streams, route profiles, vehicle and operator eligibility, road state, policies, replay results, and an incident review.
A constraint-first recovery option service.
The working slice must produce reviewable options and safe states, not commands.
Canonical disruption packet
Preserve incident, route, passenger, source, freshness, and conflict evidence.
Hard eligibility controls
Apply safety, maintenance, accessibility, labor, road, and service-floor rules before ranking.
Consequence model
Represent alternatives, critical destinations, downstream riders, accessibility, and uncertain counts.
Feasible option set
Return multiple reviewable resource and service options with explanations.
Safe operating states
Support blocked, stale, critical, degraded, verify-required, and no-safe-option outcomes.
Decision and outcome record
Link recommendation, dispatcher action, execution, correction, and realized service outcome.
The network average improves while a protected route loses service.
After average delay falls by 14%, reviewers discover that the system repeatedly cancels low-volume R17 trips. Fare-reader failures undercount riders, accessibility checks occur after ranking, and a dialysis-center shift change is visible only in an emergency call note.
REVEAL CASE COMPLICATION+
The model assigns the accessible spare bus to a high-volume corridor and leaves R17 below its protected evening service floor. A wheelchair user and dialysis-center passengers face no practical alternative. The recommendation is mathematically consistent with its objective and operationally unacceptable.
Required response: redesign eligibility, passenger consequence, critical-destination verification, evaluation, monitoring, and the release boundary. Do not repair the design with an opaque fairness weight.
Measure speed, feasibility, and distribution of service together.
The final decision must use predeclared technical, operational, passenger, accessibility, workload, and risk measures.
| Measure | What it tests | Target behavior |
|---|---|---|
| Time to feasible option | Decision support speed after controls and evidence checks. | Reduce without critical violations. |
| Infeasible recommendation rate | Maintenance, labor, accessibility, road, and service-policy failures. | Approach zero. |
| Protected service-floor violations | Whether low-volume routes are silently sacrificed. | Zero without authorized exception. |
| Accessibility mismatch | Vehicle, operator, stop, and equivalent-service feasibility. | Zero. |
| Passenger-delay burden | Delay adjusted for alternatives and consequence. | Improve without concentrating harm. |
| Dispatcher workload | Review time, verification calls, modifications, and rejected options. | Improve. |
The complete record of the deployment decision.
The lab scores discovery, evidence traceability, hard controls, working-slice behavior, evaluation, operating readiness, and executive judgment.
Decision and authority frame
Bounded decision, non-goals, service protections, and escalation rights.
Evidence and eligibility contract
Sources, freshness, consequence, hard controls, unknowns, and correction.
Working recovery service
Runnable replay with safe states, feasible options, explanations, and tests.
Pilot and operating model
Replay, shadow workflow, monitoring, support, incidents, change control, and rollback.
R17 redesign
Containment and redesign after the protected-route failure.
Executive recommendation
Evidence-backed stop, revise, shadow, narrow, or scale decision.
Enter the D2V Mock Transit guided lab.
Eight gated stages, twelve evidence sources, a runnable starter kit, ten artifacts, and a published 100-point rubric.