SafeEvent — Major Event Outreach & Preparedness Simulator

Anti-trafficking outreach preparedness simulator for major events.
Built with DBbun's Executable Publication Layer by DBbun LLC.

Submission for Call for Code AI: United Against Trafficking
Track 1: Anticipate & Disrupt — Major Event Risk & Outreach Planner

What this is

SafeEvent turns static, synthetic event-planning materials into an executable scenario simulator for major-event outreach preparedness.

The included demo uses fictional Harbor Lights Festival 2026 planning materials: venue map, schedule, transit plan, hotel/lodging clusters, volunteer staffing plan, service-provider directory, public-safety guidance, and generic city guidance PDFs.

The simulator helps planners identify vulnerability windows — moments when stranded crowds, saturated crisis counseling, housing referral backlogs, transit bottlenecks, and reduced staff capacity create elevated risk — and pre-position support before the event begins.

What this is not

SafeEvent is not a surveillance system. It does not identify real victims, real traffickers, suspicious people, or live incidents. It does not scrape active platforms or contact anyone. All included inputs and outputs are synthetic and fictional.

Scenarios

The simulator runs 9 scenarios over a 72-hour event window:

1. normal_operations
2. unusually_high_attendance
3. transit_congestion
4. volunteer_staffing_shortage
5. late_night_crowd_movement
6. service_provider_overload
7. weather_disruption_heat
8. weather_disruption_storm
9. fire_incident_h36

How to run

pip install -r requirements.txt
python Simulator.py --output ./sim_outputs

Outputs

Running the simulator generates:

• simulation_outputs.csv — full synthetic time-series output
• scenario_summary.csv — scenario-level KPIs
• parameters_used.csv — model parameters
• summary.json — metadata and run summary
• 16 figures, including:
  • attendance profile
  • transit queue pressure
  • outreach center queue
  • volunteer staffing
  • NGO referral demand
  • safe meeting point activations
  • fire incident response figures 13a–13d

Key incident-response figures

The fire incident scenario is designed as a demo of dynamic system stress:

• fig13a_evacuation_transit.png — evacuation and transit queue surge
• fig13b_medical_hospital.png — medical incidents and hospital occupancy
• fig13c_ems_availability.png — EMS availability during the incident
• fig13d_referral_surge.png — community service-provider referral surge

Ethical statement

This simulator uses zero real victim, personal, or operational data. All inputs are fictional synthetic planning documents. No individuals are identified, profiled, surveilled, or scored. Outputs are aggregate population-level synthetic projections intended for human-reviewed planning decisions only.

License

MIT License — Copyright (c) 2026 DBbun LLC. See LICENSE.