baggage-service

Airport baggage conveyor simulation handles system failures by reconstructing in-memory state from a Neo4j database during restart, ensuring continuity. Kafka-based event streams provide real-time metrics for immediate recovery and ongoing flow control.

Dangerous goods detection in baggage screening uses probabilistic screening with class-specific detection rates to flag items. The simulation handles false positives by applying offload logic to flagged baggage for subsequent review.

You simulate end-to-end baggage flow by launching the service in a configured Neo4j and Kafka environment. The in-memory conveyor model processes induction, screening, sorting, make-up, and arrival belts with per-zone throughput.

Yes, you can use Kafka and Neo4j for real-time baggage tracking in a digital twin. Kafka streams real-time events while Neo4j provides the graph database for startup convergence and state persistence across the baggage lifecycle.

Throughput is modeled across terminals using an in-memory conveyor model with zone-based throughput calculations. The simulation tracks screening outcomes and incident responses, applying system-failure effects to offload logic when necessary.

Yes, you need Neo4j to run baggage flow simulations with DG detection because it provides startup convergence and state persistence. Kafka is also required to handle the real-time event streams for throughput and incident response tracking.