Navigato AI combines Edge AI, real-time sensor data, adaptive signal control and Digital Twin simulation to help cities reduce congestion and emissions while improving public transport, pedestrian, cyclist and emergency-vehicle priority.
The operational reality facing traffic managers, transport authorities and smart-city programmes today.
Traditional signal timing ignores real-time conditions, causing congestion, missed public transport connections and unnecessary emissions as demand patterns shift throughout the day.
Sensors, cameras, PT data and traffic controllers operate in silos with no unified operational picture, making coordinated response to incidents or demand changes impossible.
Cities face EU climate targets and Vision Zero safety mandates but lack the tools to measure and optimise at intersection level, leaving a measurable gap between policy and operational reality.
Each layer is independently deployable, yet designed to work as a coherent system — from edge detection at each intersection through to citywide Digital Twin coordination.
Real-time perception at street level
Video and sensor data are processed according to privacy-by-design principles, including edge anonymisation, data minimisation and role-based access.
Low-latency communication and local intersection autonomy
Every intersection can continue operating locally while remaining coordinated with the wider city network.
Prediction, simulation and adaptive optimisation
Traffic-control recommendations must be tested, constrained and validated before live deployment. Simulation results must be clearly distinguished from validated field performance.
Controlled execution and citywide coordination
The hybrid model ensures intersections remain safe and functional at all times, while the city-level platform coordinates, learns and optimises across the full network.
Six integrated modules covering every dimension of adaptive urban traffic — from edge sensing to emissions reporting.
Real-time detection and classification of vehicles, buses, cyclists and pedestrians using Edge AI camera and radar fusion.
Adaptive signal timing and multi-intersection corridor coordination driven by live demand and Deep Reinforcement Learning.
Digital Twin for intersections, corridors and citywide simulations enabling safe what-if testing before live deployment.
Dynamic priority for buses, trams and public transport fleets to improve punctuality and reduce passenger delay.
Safety and prioritisation for cyclists, pedestrians and emergency vehicles with dedicated phase management.
Emissions-aware signal optimisation using live traffic data combined with air-quality sensor feeds and environmental targets.
Deployed across urban intersections, transport corridors, event venues, airports and logistics campuses.
Thirteen operational KPIs tracked from pilot baseline through live deployment — providing a transparent performance record for city governance.
KPIs are established in pilot phase and used to validate system performance against baseline. Projections are model-based until validated with field data.
Every city deployment follows the same eight-stage progression — from audit and simulation through to citywide orchestration.
City data and intersection audit
Digital Twin and baseline model
Simulation-only phase
Shadow-mode recommendations
Human-approved pilot
Limited live adaptive deployment
Corridor scale-up
Citywide orchestration
Every deployment begins with a simulation phase. No live traffic changes without human approval and validated performance against baseline.
Traffic-control recommendations are constrained, tested and approved before deployment. Navigato AI does not operate traffic infrastructure autonomously without explicit city authorisation and governance protocols.