MTA Testing Tech to Modernize Signal System
June 30, 2022
Solutions Aim to Reduce Subway Running Times and Improve Reliability
Today, the Transit Tech Lab announced that four companies were selected as finalists in its Signaling Challenge, a global competition calling for technologies that will help modernize New York City’s aging subway signaling system quickly and cost-effectively. The challenge seeks to increase subway capacity, efficiency and reliability by enabling trains to run closer together. The four finalists, selected from nearly 60 applications, are testing solutions that utilize emerging technologies such as artificial intelligence, LiDAR and communication-based train control (CBTC) across the New York City Transit subway system.
The Transit Tech Lab is part of the Transit Innovation Partnership, a public-private initiative created by the Metropolitan Transportation Authority (MTA) and Partnership for New York City.
Modernizing New York City’s aging signaling system will enable the MTA to run more trains, improving the customer experience for millions of people traveling within the five boroughs. The selected companies began tests in January 2022 and will present proof-of-concept results by the end of the summer.
The Signaling Challenge finalists are:
Company: 4AI Systems (New York, NY)
Technology: Using on-train artificial intelligence system to detect infrastructure, obstacles and wayside objects. The technology supports train operators in identifying obstructions, track intrusion, and equipment in need of repair.
Company: Alstom (West Henrietta, NY)
Technology: Using Urbalis, their latest technology, to enable faster communication between trains and bi-directional train movement. The product has a simplified, train-to-train architecture that eliminates the need of wayside equipment, reduces installation times, improves operational headway and speed, and lowers life cycle costs.
Company: Luminar (Orlando, FL)
Technology: Using long-range LiDAR technology coupled with perception software from their partner, Seoul Robotics, to accurately position a train underground, including areas where GPS traditionally fails. The product provides accurate arrival and departure data and can be further trained to detect hazards and structural damage on the tracks.
Company: Ouster (San Francisco, CA) in partnership with Lux Modus (Calgary, Canada)
Technology: Using digital LiDAR sensors to collect millions of high-resolution 3D geospatial data points on a Track Geometry Car. The technology provides a digital twin and detects structural damage, infrastructure decay and foreign objects on the tracks.
NYC Transit President Richard Davey said, “Increasing reliability is paramount to improving the customer experience and bringing more people back to the subway. This diverse group of pilot programs provides a variety of solutions towards achieving this goal, and we look forward to seeing the results.”
Natalia Quintero, Senior Vice President of Innovation at the Partnership for New York City said, “New York’s transit system is in the midst of tremendous transformation, and we are delighted to be helping accelerate the pace of positive change. With visionary leadership at the helm of the MTA and cutting-edge technology, New York City is well positioned to build the transit system New Yorkers deserve.”
About the Transit Tech Lab
The Tech Lab program is part of the Transit Innovation Partnership, which yielded the award-winning MTA Live Subway Map and was established by the MTA and the Partnership for New York City to bring private sector innovation to improve public transit.
Since 2018, the program has put the New York metropolitan region at the forefront of transit innovation, which promises to support the modernization of public transit and to transform the customer experience. Winners of previous competitions include Remix, a collaborative digital platform used to redesign the bus routes, and Axon Vibe, which built the Essential Connector smartphone app to help essential workers plan journeys during overnight subway disinfection closures.