CrowdSynthetic

Product Introduction

1. Definition: CrowdSynthetic is an open-source AI crowd safety simulator specializing in predictive congestion analysis for mass gatherings. It falls under the technical category of agent-based simulation software with real-time risk analytics.
2. Core Value Proposition: It prevents crowd disasters by forecasting dangerous congestion patterns before they escalate, enabling proactive safety interventions at events like concerts, festivals, and stadiums.

Main Features

1. Real-Time Movement Visualization:
   • How it works: Uses OpenCV-based rendering to display individual avatars moving through configurable venue layouts (stages, exits, zones). Simulates physics-based crowd flow dynamics.
   • Technologies: Python, OpenCV, JSON-based layout configuration.
2. Dynamic Risk Heatmaps & Zone Scoring:
   • How it works: Generates color-coded density heatmaps by calculating per-pixel occupancy. Assigns numerical risk scores to zones based on capacity thresholds and movement trends.
   • Technologies: Real-time density algorithms, threshold-triggered alerts.
3. Automated Evacuation Logic:
   • How it works: Triggers EVAC mode when risk thresholds are breached, rerouting avatars to exits using pathfinding logic. Freezes simulation post-evacuation for forensic analysis.
   • Technologies: State-machine architecture, collision-avoidance systems.
4. Predictive Congestion Analytics:
   • How it works: Forecasts saturation points (e.g., "FRONT zone critical in 90s") using velocity vectors and density trend analysis.
   • Technologies: Time-series prediction models, trajectory extrapolation.
5. Comprehensive Data Logging:
   • How it works: Exports timestamped risk metrics (density scores, zone status) to CSV/JSON for post-event auditing and model refinement.
   • Technologies: Python logging modules, structured data pipelines.

Problems Solved

1. Pain Point: Reactive crowd management leading to crushes/stampedes (e.g., Hajj, Love Parade disasters). Keywords: crowd crush prevention, stampede risk mitigation.
2. Target Audience: Event safety managers, venue operations teams, public security agencies, and urban planners designing mass-gathering spaces.
3. Use Cases:
   • Pre-event evacuation planning for stadium concerts
   • Real-time risk monitoring during religious pilgrimages
   • Post-incident forensic reconstruction for safety audits

Unique Advantages

1. Differentiation: Unlike CCTV-based systems (reactive), CrowdSynthetic predicts future hotspots. Outperforms static CAD models by simulating behavior-driven crowd dynamics.
2. Key Innovation: Fusion of real-time heatmapping with predictive AI analytics and open-source modularity—enabling customization for niche venue layouts.

Frequently Asked Questions (FAQ)

1. How accurate is CrowdSynthetic's congestion prediction?
   Accuracy depends on venue layout calibration and avatar behavior parameters, but its trend-based forecasting identifies risk zones 60-120 seconds before critical density.
2. Can CrowdSynthetic integrate with existing venue security systems?
   Yes, its JSON-based API and CSV/JSON outputs allow interoperability with IoT sensors, CCTV feeds, and public address systems.
3. What computational resources are needed to run simulations?
   Requires Python 3.9+, OpenCV, and moderate GPU resources for real-time rendering. Scales from laptops to cloud clusters via parallelization.
4. Is CrowdSynthetic suitable for permanent installations?
   Absolutely. Its logging and evacuation logic support both pre-event planning and live deployment via continuous simulation.
5. How does the open-source model benefit users?
   Organizations can customize evacuation protocols, integrate proprietary data sources, and adapt models to unique venues without licensing costs.