Crowd Flow Optimization — real-time crowd flow prediction and orchestration

Executive summary

Prototype crowd flow orchestration system designed for mega-events (concerts, stadiums, festivals). The goal: predict congestion 10 minutes ahead, trigger automated recommendations for field teams and maintain 99.9% availability during critical phases. End-to-end event-driven architecture, from sensor to operator dashboard.

Business problem

Mega-event organisers manage crowds of 50,000 to 100,000 people with reactive, non-predictive tools. Congestion incidents are detected too late, redirection decisions take several minutes, and field teams lack real-time contextual information. The human and reputational cost of a major incident is considerable.

Solution

Real-time 6-layer pipeline: multi-source collection (IoT sensors, anonymised cameras, ticketing), transport via Kafka MSK, Apache Flink stream processing (30s windows), ML scoring via TensorFlow Serving, decision engine with YAML fallback, Next.js operator dashboard with 1s WebSocket. Each layer is independently decoupled and resilient.

Target KPIs

Technical architecture

Event-driven architecture with 6 decoupled layers. The collection layer aggregates sensor streams via AWS IoT Core and MQTT. The transport layer uses AWS MSK (managed multi-AZ Kafka) with Schema Registry for contract validation. Apache Flink on KDA handles stream processing with 30-second sliding windows and exactly-once guarantee. TensorFlow Serving on ECS Fargate handles inference with auto-scaling. The FastAPI decision engine combines ML scoring and YAML fallback rules. The Next.js dashboard receives pushes via WebSocket API Gateway.

Competitive advantages

No SaaS solution on the market combines 10-minute prediction, a decision engine with deterministic YAML fallback, and a real-time operator dashboard at under 200ms end-to-end latency. The design prioritises resilience: if the ML model is unavailable, YAML rules guarantee operational continuity. The architecture is designed for the constraints of live events: predictable load spikes, zero tolerance for outages during critical phases.

Risks and mitigations

Primary risk: quality and availability of field sensors. Mitigation: multi-source architecture with graceful degradation if a stream becomes unavailable. Second risk: network latency in large venues (saturated Wi-Fi). Mitigation: edge gateway with local buffer and batch transmission. Third risk: model false positives generating unnecessary alerts. Mitigation: configurable confidence threshold and human validation for critical-level alerts. Fourth risk: adoption by field teams. Mitigation: simplified operator UX and offline mode.