Step 01Setup
Capture
We use the cameras at your junctions and corridors — existing or new. They feed trafficNET; nothing changes for road users, and there's no disruptive roadwork to install sensors.
Smart City
trafficNET
Intelligent traffic monitoring. Computer vision and predictive analytics that map how vehicles and pedestrians really move — so cities optimise mobility on real data.
BuiltforCities, traffic authorities & urban-mobility planners.
trafficnet.dunavnet.eu — NFronta 1 — live
How it works
From camera feeds to better mobility, in four steps.
trafficNET precisely monitors vehicle and pedestrian movement with computer vision and analytics, enabling more efficient planning of traffic infrastructure and optimised flow across the city.
Step 02Real time
Detect
Computer-vision algorithms detect and classify vehicles and pedestrians precisely, tracking each one's movement — counts, types and turning movements, junction by junction.
Step 03Continuous
Analyse
Predictive analytics map traffic patterns over time — volumes, turning movements, pedestrian flows and congestion — so you see how the network really behaves, not a snapshot.
Step 04Ongoing
Optimise
Planners make better infrastructure decisions and tune flow on real data: retiming signals, easing congestion and improving pedestrian safety across the city.
From camera feeds to confident infrastructure decisions — across the network
Solution portfolio
What your traffic team actually watches.
From the live dashboard — totals, turning-movement splits and flow over time, per junction — to the computer vision detecting and classifying every vehicle, these are the screens your mobility and planning teams use.
app.trafficnet.dunavnet.eu — Traffic dashboard
Works with your cameras
Built on the cameras and systems you already run. Not new street furniture.
trafficNET reads from your existing IP / CCTV and traffic cameras, processes them with computer vision, and feeds anonymous counts and analytics into your management centre, displays and planning tools.
IP / traffic cameras
Existing CCTV
Computer vision
Signal controllers
Junction, corridor or city-wide? trafficNET scales from a single intersection to a whole network. Bring your priority sites to the discovery call.
How trafficNET fits
Reference architecture
Your city
Cameras
IP / CCTV you already run
Video feed
trafficNET
trafficNET
Computer vision + predictive AI
Counts & patterns
trafficNET
Dashboard — API
Volumes · turning movements · patterns
REST API · CSV
Your city
Your planners
Signals — safety — infrastructure
On the street
Where trafficNET keeps watch.
Real corridors, real flows. The junctions trafficNET monitors, the crossings it helps make safer.
Deploy three ways
Pick the model that matches your network and ops posture.
SaaS
Hosted by us — EU region
Edge unit at the junction, dashboard in our EU cloud. Fastest start. Monthly billing. ISO 27001-controlled.
- Live in 2–4 weeks
- Monthly — per junction
- EU data residency
Managed AzureMost chosen
Your tenant — we operate
Deployed inside your Azure tenant. Your data residency, your compliance posture, our 24/7 operations team.
- Your tenant
- We run the ops
- Co-managed SLA
Modular / custom
Start small, grow in
Begin with the junctions and corridors that matter most and add capabilities as you grow — connecting to your wider analytics, automation and decision-support tools via the DunavNET Innovation Studio.
- Progressive rollout
- Ecosystem-ready
- Studio engagement
Customer story
What one corridor revealed once it was actually measured.
Urban corridor — pilot 2025 — figures indicative
"We'd been planning a junction off manual counts taken twice a year. trafficNET showed the real turning movements and pedestrian peaks — the signal retiming we chose was completely different, and congestion dropped."
City mobility planner
Urban corridor pilot — multi-junction — Central Europe
24/7Vehicle and pedestrian movement, continuously
LiveMovements measured, not estimated
FullPedestrian flows factored into planning
0 PIICounts and patterns only — no identification
Common questions
What cities usually ask before they sign.
Vehicle and pedestrian movement at junctions and along corridors — volumes, vehicle types, turning movements, pedestrian flows and congestion — mapped as patterns over time using computer vision and predictive analytics.
No. trafficNET counts, classifies and tracks movement — it does not identify individuals, read number plates, or store any personal data. Processing is done on anonymised video frames; no footage is retained.
Usually not. trafficNET is designed to work with your existing IP and CCTV camera infrastructure. If a specific junction or corridor has no coverage, we scope the additional hardware as a separate line item during the discovery call.
By surfacing the real pedestrian peaks, turning movements, and vehicle types at each junction — not estimates from periodic manual counts. Planners use that data to retime signals, redesign crossings, and prioritise interventions where the data shows they matter most.
Yes. trafficNET outputs structured counts and analytics via REST API and CSV export, and connects to signal controllers, public information displays, GIS tools, and your existing traffic management centre — no new street furniture required.
Generic counters give you totals; trafficNET gives you turning movements, vehicle types, pedestrian flows, and patterns over time — per junction, per approach. You see how the network behaves, not just how busy it was. DunavNET also runs the analytics and model tuning, not just the hardware.
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