# UrbanFlow AI

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Meet Rabadiya](https://cerebralvalley.ai/u/Rabadiya)
- **GitHub:** https://github.com/rabadiyameet073/urbanflow-traffic-intelligence
- **Demo video:** https://drive.google.com/drive/folders/1E137QRz0haeaIuQS09dbZFY1FEH93qeR?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2035

Over 80% of municipal traffic signals around the world still run on rigid, pre-timed schedules designed in the 1970s. This outdated infrastructure costs the global economy over $88 billion annually in wasted fuel and lost productivity. But the far more tragic cost is measured in human lives: emergency responders routinely find themselves trapped at red lights while empty cross-streets get green time. In cardiac arrest, stroke, and severe trauma emergencies, every 60 seconds of transit delay reduces patient survival rates by 7% to 10%. Optical sirens and strobe detectors fail at distance, create severe blind spots, and cause dangerous secondary collisions when cross-traffic is caught off-guard.

UrbanFlow AI solves this crisis by pairing real-time adaptive traffic intelligence with an uncompromising, mathematical safety kernel. 

Rather than theoretical simulations, UrbanFlow AI is built as an industrial-grade digital twin platform. In a live browser session, it runs dual 8-lane intersections simultaneously—feeding the identical pseudorandom vehicle arrival seed into both a legacy fixed-time controller and our adaptive controller. Judges and city planners can see the proof side-by-side in real time: adaptive queue-pressure balancing slashes average delays by up to 24.8% and eliminates queue spillbacks, while discharging more total vehicles across every single tested traffic demand scenario.

Core Architectural Innovations:
1. Microscopic Multi-Class Physics Engine: High-fidelity Intelligent Driver Model (IDM) car-following physics across 8 approaches (through/right lanes and dedicated left turns) with 5 vehicle classes (passenger cars, buses, delivery trucks, bicycles, and emergency vehicles). Running at 10 Hz inside a background Web Worker with zero-copy binary ArrayBuffers, it delivers 60 FPS graphics without blocking the user interface.
2. Single-Writer Deterministic Safety Kernel: We believe AI should never directly command physical city infrastructure. UrbanFlow enforces a strict mathematical boundary: heuristic algorithms and Meta Llama models generate advisory requests only. Only our deterministic Safety Kernel can mutate signal states, mathematically enforcing 6 critical invariants on every 100ms tick: minimum green (4.0s), mandatory yellow clearance (3.5s), all-red buffer (2.0s), anti-starvation limits (120s), and absolute phase exclusivity.
3. Cryptographically Authenticated Emergency Vehicle Preemption (EVP): Real-time V2X distance tracking with constant-time HMAC-SHA256 signature verification, single-use replay nonces, and storm-guard rate limiting. The system enters a PRECLEAR state through safety buffers and awards a conflict-free EMS green wave, allowing ambulances to cruise through intersection boxes without stopping.
4. Edge Gateway & Cloud Telemetry: Powered by Cloudflare Workers and MongoDB Atlas for sub-10ms operational queries, combined with PySpark Structured Streaming for sliding-window anomaly analysis.

UrbanFlow AI proves that municipal infrastructure can achieve transformative efficiency without ever compromising public safety.

## More from Meta Global AI Developer Hackathon

- [Answer 42](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2032)
- [CHINCHIRU_35](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2033)
- [Hyperpod](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2034)
- [LlamaForge](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2036)
- [Iyanu Arowosola](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2037)
- [BoOngThuoi](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2038)

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