traffic jam in 1920

Trafluid, the future of transportation


Transportation's Future

AI-Powered Traffic Lights

We are developing a new generation of traffic lights powered by state of the art reinforcement learning algorithms, and analyse common traffic patterns in order to calculate the most optimal traffic light state across a network of intersections. The scalability of our platform will allow the deployment of those smart traffic lights at millions of intersections, in thousands of cities across the world.

Traffic jam in 1920
From England in 1926...
Traffic jam in 2020
... to Bengaluru in 2020

A century old problem

Traffic Management Has Been an Issue… For a Century Now!

Legacy traffic lights are oblivious to the actual traffic. This causes drivers waiting endlessly at intersections. This causes traffic jams, congestion, pollution, aggravated public and wasted productivity at the city level. Moreover, the lack of traffic lights at numerous intersections is a safety hazard. You need to turn onto a road but you can not see the oncoming traffic. Safety is compromised.

A World of Traffic

Market and Opportunity for Growth

The total cost of traffic congestion in 2019 in USA alone was $88 Billion. Global intelligent traffic management system market size was estimated at $25.4 Billion in 2020 and is expected to reach $37.6 Billion in 2027.


Municipalities and cities

Trafluid seamlessly integrates smart transportation into legacy cities with pretimed traffic lights and has been proven to work even with low penetration rates.


Smart and smarter city projects

Masdar City, Neom, and other ambitious smart city project will need Trafluid in order to build from the ground a new generation of interconnected road network.


Closed and controlled spaces

Trafluid can be used in airports, seaports, university campuses, military bases, and other places that require monitoring and managing large vehicle fleets.


Connected and autonomous cars

Trafluid will remove the burden that autonomous vehicles have when determining a traffic light state using visual computing (errors, low visibility etc...).

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