Yunex Traffic, a developer of intelligent traffic systems, has announced the signing of a partnership agreement with Lyt, a developer of intelligent connected traffic technology solutions.
The two companies hope to leverage their joint intelligent transportation technologies to reduce congestion for commuters and emergency responders.
Lyt has developed a cloud-based software platform that uses connected vehicle and machine learning technologies to prioritise vehicle flow in a city and across a road or corridor.
The idea behind this is that, by optimising public transport, emergency vehicles and other types, the solution can enable shorter travel times, reduced congestion, improved air quality, longer lives and more reliable mass transit.
Traffic agencies can now pair Yunex Traffic’s suite of solutions with Lyt’s Emergency Vehicle Preemption solution, which provides a green light for every emergency vehicle.
The company claims this solution is cheaper than other alternatives on the market. These solutions harness the power of a single secure edge device installed in traffic management centres, which is designed to enable emergency vehicles to communicate directly with a city’s traffic signals through the Lyt.speed cloud platform.
“At Yunex Traffic, we are committed to innovation and creating technologies to push transportation forward,” said Michael Gaertner, head of product and systems, DOP for Yunex Traffic US.
“[Lyt’s] vision parallels ours, focused on solving issued with impactful solutions rather than creating problems to solve.
“We couldn’t be more excited.”
“By shifting from line-of-site detection systems to Lyt’s bird’s-eye-view optimisation system, traffic signals turn green well in advance of emergency vehicles, producing safer intersections for everyone,” said Tim Menard, CEO and founder of Lyt.
“Along with Yunex Traffic and their leading technologies, our combined solutions will serve as a new benchmark for the way in which cities leverage data, technology and AI in a budget-affordable way to better understand the flow of traffic and also the way in which machine learning can quickly adapt to new traffic flow for better urban quality of life.”