Iteris is to deploy its Vantage Vector hybrid detection system at 60 intersections in the city of Modesto in California as part of a $3.3 million deal.
Vantage Vector is an all-in-one detection sensor that combines video and radar for stop bar and advance zone detection.
The company says the city will be able to differentiate between vehicles, bicyclists and pedestrians to improve traffic flow and safety for all road users while saving money.
Vantage Vector is expected to provide data and insights on trends in traffic volumes and modes of transportation that will enable the city to adjust traffic signal timing to minimise congestion and delays.
The project will also save the city money by eliminating the need for in-ground hardware and its associated higher costs of construction, the company adds.
The system also provides data for smart mobility applications, automated traffic signal performance measures software and adaptive traffic control systems.
It has high-precision radar sensing technology that provides total coverage of the 'dilemma zone' - the point at which a driver approaching a yellow signal light has to decide on whether to attempt to stop and risk a rear-end collision or proceed into the intersection, potentially running a red light and risking a right-angle crash.
According to Iteris, Vantage Vector’s dilemma zone detection capabilities help to reduce the risks of rear-end and right-angle collisions by either extending a signal phase to give drivers more time to react or enabling an all-red phase, pausing traffic until the intersection is cleared.
Vantage Vector is a component of Iteris’ ClearMobility Platform, a solution to continuously monitor, visualise and optimise mobility infrastructure.
ClearMobility applies cloud computing, artificial intelligence, advanced sensors, advisory services and managed services to help ensure roads are safe.
Iteris' $3.3m intersection deal solves dilemma
City of Modesto, California, will improve traffic flow while saving money, says Iteris
Detection, Monitoring & Machine Vision / May 18, 2021
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