Federal Grant Awarded to NJ TRANSIT to Develop Artificial Intelligence System for Grade Crossing Safety

Written by New Jersey Transit, Agency Communications
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NEWARK, N.J. –– NJ TRANSIT has been awarded a federal grant to work with Rutgers University's Center for Advanced Infrastructure and Transportation (CAIT) to implement AI-powered safety systems on light rail vehicles at grade crossings.

NJ TRANSIT President and CEO, Kevin S. Corbett, said “Alongside Rutgers CAIT, we are working to set a new standard in transportation safety aimed at preventing grade-crossing incidents leveraging the power of artificial intelligence.”We thank the USDOT for this grant award, which allows NJ TRANSIT to continue to introduce innovative technologies that enhance safety in and around the communities we serve.”

“By investing in innovative solutions for transportation safety, we can protect commuters and pedestrians and modernize New Jersey’s infrastructure,” said Congressman Rob Menendez, who represents the district where the project will begin. “I am proud to see NJ TRANSIT and Rutgers University use federal funding to implement AI-powered systems and enhance grade crossing safety along the Hudson-Bergen Light Rail.”

“Equipping rail grade crossings and critical infrastructure with AI-powered technologies allows NJ TRANSIT to collect and analyze previously unattainable safety data,” said CAIT Director Dr. Ali Maher. “This information will serve as the foundation for data-driven safety countermeasures that will be deployed to protect communities, reduce service delays, and enhance rail reliability throughout New Jersey. CAIT is proud to support NJ TRANSIT and the USDOT in this innovative grant.”

The primary focus of this initiative is to significantly enhance safety, while reducing accidents at light rail grade crossings and on rights-of-way. This project strategically aligns with the goals of the Strengthening Mobility and Revolutionizing Transportation (SMART) grant program and aims to elevate the state of New Jersey’s transportation system.

In Stage 1 of this project, NJ TRANSIT and Rutgers University will prototype a tailored artificial intelligence-powered technology solution consisting of stationary cameras at five light rail grade crossings, and forward-facing cameras in one Hudson-Bergen Light Rail vehicle. This technology will improve safety and detection, while offering valuable guidance for future infrastructure enhancements in other areas of mass transit. The Stage 1 study will culminate in a network-wide implementation plan geared towards enhancing pedestrian and vehicle safety.

The system will leverage cutting-edge AI algorithms, including deep learning, to analyze real-time camera video data from light rail grade crossings (captured by stationary cameras) and rights-of-way (captured by forward-facing cameras in light rail vehicles) using Edge Computing technologies. For grade crossing safety monitoring, the AI system will identify the activation of warning devices (e.g., signals and gates) and subsequently detect safety events, considering factors such as trespasser type (e.g., pedestrian or vehicle), time of occurrence, movement trajectory, weather conditions, proximity to the train’s arrival, and other critical factors.

In Stage 2, NJ TRANSIT will proceed with the implementation of the AI-powered technology at 50 grade crossings and five light rail vehicles on its light rail systems. These systems will increase safety, reduce delays, and ensure a wide array of communities across the state will benefit from improved access to safe transportation options.

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