flight-service

Model flight state transitions by advancing flights through a deterministic finite state machine covering scheduled, boarding, departed, airborne, landed, and arrived states. This ensures observable, testable lifecycle progression integrated with Kafka messaging.

Kafka handles event-driven flight lifecycle updates by propagating state changes and cascade delay effects across paired flights. Neo4j persists these transitions, creating analytics-ready data flows for simulation orchestration.

Yes, you can simulate gate and runway allocation conflicts. The system handles slot allocation, gate conflicts, and reassignments dynamically under varying weather and incident conditions within the digital airport twin.

The best way to model aircraft turnaround and cascade delay propagation is using an event-driven state machine that models inbound and outbound turnarounds and ripple delay effects across paired flights with optional event propagation.

Kafka and Neo4j are integral to the flight-service simulation. Kafka provides event-driven messaging for state updates, while Neo4j handles persistence, ensuring the state machine remains observable and analytics-ready.