Censys used its AI-powered CensWise platform to analyze 40GB of historical MTA drone footage covering 3.8 miles of track. In 17 hours, the system identified five key maintenance issues, including over 300 vegetation encroachments. Censys estimated that this automated inspection could save 30 hours of manual inspection time, $800/mile of inspection, and would eliminate the risk of service disruption caused by on-track personnel. Censys highlighted the potential of AI and drone-based technology to enable more frequent and cost-effective inspections, enhance worker safety, and support a shift toward predictive maintenance.

FlipAI applied its AI platform to clean and visualize the PANYNJ's Ports Department fleet inspection and maintenance data, analyzing over a year of records for 194 vehicles. The tool uncovered ~$300K in procurement savings by fleet rightsizing, $20K in maintenance efficiencies, and $1.7M in unused lease costs, while saving significant staff time through automated analysis. FlipAI demonstrated how AI can enable more strategic resource planning, augment staff decision-making, and support predictive maintenance across the PANYNJ's vehicle portfolio.

Kinexio installed 48 NFC tags at five critical NYCT subway and three PANYNJ World Trade Center locations to digitize inspections and enable real-time verification, logging, and safety monitoring. At NYCT, 27 tags were placed in elevator and escalator motor rooms across five subway stations, where 10 MTA technicians completed over 50 digital inspection logbook entries. At the World Trade Center, 19 tags were installed supporting 2 security booth inspections and 11 drain inspections by PANYNJ staff and contractors. Kinexio’s tamper-proof tags enabled verifiable inspections, eliminate paper logbooks, and offered a user-friendly system.

Previsico demonstrated how its flood forecasting and sensor technology could help the MTA and PANYNJ service disruptions caused by stormwater flooding. Its Instacast forecasting model retroactively analyzed the September 2023 Ophelia flood across 45 MTA stations at 820 feet resolution and the September 2021 Ida flood at four major PANYNJ airports at 82 feet resolution, accurately forecasting impacted locations with 93% and 100% accuracy, respectively. Separately, two sensors were installed at Coney Island Yard and Teterboro to provide real-time water level alerts. Combined, Previsico’s forecasting and sensor solutions can enable precise targeting of high-risk areas, helping reduce false alarms, minimize operational disruptions, and guide smarter resilience investments.

Routora evaluated the DOT’s manual, paper-based process for assigning, scheduling, and routing inspection requests, and demonstrated how its digital platform can streamline these tasks into a single, efficient workflow. By mapping the inspection workflow, Routora showed how digitized scheduling and optimized routing could reduce inspector drive time by 20% and minimize manual planning. The system generates stop sequences that inspectors can follow using their preferred navigation app, and groups inspections by urgency and geography — offering the potential to increase inspection volume and accelerate inspection workflows.

SafetyCulture worked with NYCT’s Elevator & Escalator (E&E) team to digitize a legacy elevator inspection process using its easy-to-use Inspections feature. The tool enabled teams to capture data in real time, reducing double entry into internal systems and making records easier to find and act on. Twenty-five staff participated in the pilot and rated the tool 4 out of 5 for user-friendliness, with strong interest expressed in features like talk-to-text, offline functionality, and future integration with the agency’s EAM system.

SahayAI leveraged their proprietary sensor “LARR-E” and AI-powered analytics to collect detailed track data from the Avenue X subway platform at Coney Island Yard. The team conducted seven test runs, capturing 95 GB of data and achieving positional accuracy within 0.5 feet at speeds up to 65 mph. The system generated 38 automated alerts, flagged fault hotspots for early inspection, and identified 55 instances of vegetation encroachment within half a mile of track. If deployed on revenue trains in the future, Sahay AI’s system could offer a scalable way to monitor the entire subway network every 48 hours — detecting defects in real-time and enabling earlier, targeted maintenance to reduce service disruptions.

Tomorrow.io developed a plan to demonstrate how its hyperlocal weather intelligence platform can enhance weather-sensitive operations across PANYNJ airports by delivering real-time, high resolution, fully customized analytics. The company will collaborate with airport stakeholders to define high-impact use cases—such as runway snow accumulation thresholds and precipitation-type changes—and scoped a pilot to implement up to 15 customized alerts across the NY airports. With tailored dashboards and lead-time alerts, the platform aims to enable earlier, more targeted deployment of crews, improving safety while reducing response costs. Tomorrow.io projects the potential to lower winter operations costs by up to 20% through smarter, faster decision-making during disruptive weather events.

TwinKnowledge demonstrated how their AI can streamline construction drawing reviews by detecting changes and standards noncompliance with high speed and accuracy. The team tested its technology across twelve MTA projects and three PANYNJ projects, cutting excess review time for the MTA and PANYNJ by up to 8x and reducing review cycles by up to 3 days. TwinKnowledge AI Agents understand and identify graphical, text, and symbol modifications with 99-100% accuracy. By automating time-consuming comparisons and flagging discrepancies instantly, TwinKnowledge has the potential to eliminate manual review bottlenecks, accelerate project timelines, and strengthen quality assurance across capital construction workflows.