AI-powered, computer-vision-driven urban traffic intelligence that propagates real-time intersection data — including pedestrian telemetry — across city road networks in milliseconds.
Traditional traffic signals operate in isolation. Each intersection reacts only to the vehicles directly in front of it — by the time congestion builds, it's already too late to adapt. Pedestrian data is ignored entirely. The result: urban drivers lose an average of 45 hours per year to congestion, and cities emit needless CO₂ waiting for lights that don't know what's coming.
45h
Lost per driver/year
0%
Intersections that share data
$87B
Annual congestion cost (US)
3.8B
Smart signal market by 2028
02 · The Solution
A synaptic network of seeing intersections
SYNTRAC treats every intersection as a visual AI node. Each node sees its environment through computer vision, reasons with on-device ML, and fires a rich data packet — vehicles, pedestrians, queue lengths, incident flags — to the next intersection via V2X over 5G. Downstream signals pre-adapt before traffic arrives, creating a city-wide intelligent green wave.
STEP 01
Visual capture
4K cameras + LiDAR at each node. YOLOv11 detects vehicles, pedestrians, cyclists in real time.
STEP 02
Edge inference
On-device GPU runs density estimation, queue length prediction, and ped crossing intent models.
STEP 03
Synaptic broadcast
Compressed telemetry packet fired over 5G V2X to adjacent intersection nodes in <30ms.
STEP 04
Cloud DRL
DDPG reinforcement learning engine computes optimal phase plan for receiving node.
STEP 05
Pre-adaptation
Next intersection adapts signal timing before vehicles arrive. Cascade propagates city-wide.
03 · Visual AI / Computer Vision Stack
Seeing the city in real time
SYNTRAC's intelligence layer is built on a multi-model visual pipeline. Each intersection node runs three specialized vision models in parallel on an edge TPU, feeding a unified scene graph to the cloud reasoning engine.
CV · DETECTION
YOLOv11 Object Detection
Real-time multi-class detection of vehicles, pedestrians, cyclists, and e-scooters. 30+ fps at 4K. Custom-trained on urban intersection datasets.
CV · DENSITY
Crowd Density Estimation
CSRNet-based convolutional density map generation. Estimates pedestrian load per crossing zone without individual tracking — privacy-preserving by design.
ML · PREDICTION
Queue Length Forecasting
LSTM temporal model predicts vehicle queue build-up 60 seconds ahead using optical flow history and incoming V2X telemetry from upstream nodes.
CV · INTENT
Pedestrian Crossing Intent
Pose estimation + trajectory prediction (PedGraph+) infers whether a pedestrian intends to cross. Extends green-man signal proactively for vulnerable users.
ML · CONTROL
DDPG Reinforcement Learning
Deep Deterministic Policy Gradient agent optimizes signal phase timing across the full intersection network as a continuous action space — proven to cut congestion up to 50%.
ML · ANOMALY
Incident Detection
Unsupervised anomaly detection on optical flow patterns flags accidents, debris, and stalled vehicles within 2 seconds. Triggers emergency vehicle preemption cascade.
04 · Key Differentiators
What makes SYNTRAC different
Core IP
Cross-intersection visual memory
SYNTRAC is the only system that transmits a compressed visual scene graph — not just vehicle counts — between intersections. The receiving node "sees" what the upstream node saw, enabling predictive rather than reactive control.
Pedestrian AI
Pedestrian telemetry as first-class signal
Pedestrian density, crossing intent, and mobility-aid detection are factored into the inter-node packet. No current commercial system propagates pedestrian data between intersections.
Architecture
Autonomous edge fallback
Each node operates fully autonomously if the cloud or V2X link is lost. Local RL agent maintains optimized timing. No single point of failure — unlike centralized competitors.
Privacy
Zero individual tracking
All visual inference happens on-device. Only aggregate density maps and anonymized telemetry leave the node. GDPR and CCPA compliant from day one — a critical differentiator for European municipal contracts.
05 · Market Opportunity
A $3.8B market growing fast
Smart traffic signal infrastructure is projected to be a $3.8B global market by 2028. Municipal procurement cycles are 3–7 years, creating durable revenue moats once installed. SYNTRAC targets Tier 1 city contracts first, then Tier 2 via a SaaS dashboard licensing model.
N. America
$1.4B
Europe
$1.1B
Asia-Pac
$1.8B
MENA
$0.5B
06 · Roadmap
From prototype to city OS
Q3 2026
Prototype · 4-intersection pilot
Deploy edge nodes at 4 connected intersections. Validate cross-node visual telemetry, DDPG signal optimization, and pedestrian intent model in a controlled urban environment.
Q1 2027
City pilot · 50-intersection corridor
Partner with a Tier 1 city transport authority. Deploy along a major arterial corridor. Measure congestion, pedestrian safety KPIs, and emissions delta vs. control group.
Q4 2027
SaaS platform launch · SYNTRAC Dashboard
Launch city operator dashboard with real-time visual analytics, incident feed, pedestrian heatmaps, and API access for third-party urban planning tools.
2028
Scale · 5 cities, 500+ intersections
Expand to five cities across two continents. Integrate autonomous vehicle V2X handshake protocol. Begin white-light signal trials for AV coordination.
2029+
City OS · Digital twin integration
SYNTRAC becomes the real-time sensory layer for municipal digital twins — feeding traffic, pedestrian, air quality, and incident data into city planning simulations.