OMNIQ’s Proprietary AI Based Vehicle Recognition Systems Selected for Urban Traffic Management and Regulation Enforcement Projects
- omniQ’s patented Neural Network algorithms HOV Traffic Management product was approved to advance to the next phase of the pilot – the Operational Phase.
- Public transportation and High Occupancy Vehicle lanes are essential in the effort to resolve traffic congestion which plagues all major cities
- Drivers in New York spent 91 peak hours stuck in traffic.
- Traffic congestion costs an average $100 billion over five years.
- However, Studies have shown that 80% of the vehicles in unmonitored High Occupancy Vehicle (HOV) lanes are in violation of the law.
- omniQ’s HOV Developed AI based solution achieves quicker traffic flow saving time and money.
OMNIQ Corp., an object identification company and provider of Artificial Intelligence (AI) and IoT-based solutions, announced that the Company has been selected to advance to the Operational Phase of the Pilot. omniQ’s Innovative technology is geared for urban traffic enforcement along exclusive lanes – public transportation and high-occupancy vehicle (HOV) lanes. omniQ’s technology addresses a severe problem as a global traffic survey (in 2017) points out, drivers in New York spent 91 peak hours stuck in traffic. This traffic congestion will cost an average of $100 billion over the next five years.
Both public transportation and HOV lanes are regulated exclusive lanes, commonly used by Urban planners in an effort to resolve traffic congestion which plagues all major cities. However, a regulatory system must be enforceable. omniQ is providing solutions for an effective automated enforcement.
The systems, based on state of the art imaging recognition units, will be deployed along public transportation lanes leading to the city and along central streets in the city center and neighboring communities. The system shall recognize the registration of the vehicle and its type. Violators of bus exclusive lanes will be detected and reported to a central command and control center. This will deter the abuse of the public transportation infrastructure, increase its efficiency, reduce travel times, thus leading to an increase of the number of passengers and the shift from private cars to buses.
For a second aspect of privileged lane enforcement, omniQ is testing its newly developed system for High Occupancy Vehicle (HOV) lanes. The patent pending system employs special imaging units and deep-learning analytics to count the number of occupants for restricted lanes. The system shall report the vehicle registration and the number of its occupants. Single driver violators will be reported and fined. It will reduce the abuse of the exclusive lanes, increase their efficiency, thus encouraging shared driving and reduce the load from saturated highways. There is growing demand for global vehicle occupancy detection systems, anticipated to exceed market value of approximately $180 million through 2030.
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“Smart Cities mainly rely on Traffic Management infrastructure and enforcement. omniQ is determined to be a significant player in this field, providing innovative product infrastructure to address the needs of Smart Cities. Our commitment to improve traffic flow, by employing novel technologies in a diversity of mobility projects, increases the efficiency of urban transportation,” said Shai Lustgarten, CEO of omniQ. “This encouraging development is another step forward for our solutions offering to urban traffic enforcement and is in line with our continuing penetration of our services offered to a broad range of applications and used by institutions, government agencies and municipalities worldwide”
omniQ’s AI-based machine vision VRS solution uses its patented Neural Network algorithm imitating the human brain for pattern recognition and decision taking. More than 17,000 omniQ’s machine vision cameras are installed worldwide including approximately 7,000 in the US. The Company’s proprietary technology consists of hardware and software components to create touchless and intelligent traffic operations.
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