The 28th Annual NJDOT Research Showcase brought together New Jersey’s transportation community. The event took place in-person at Mercer County Community College—with a livestreaming option—from 9:30 AM to 3:00 PM on October 28, 2026. It highlighted ongoing research and technology transfer initiatives conducted by NJDOT partners, including institutions of higher education, public agencies, and private-sector organizations.

This year’s theme, “The Road Ahead: AI and the Future of Transportation” shaped the morning plenary session and much of the subsequent event. In the afternoon, the Showcase featured presentations on infrastructure, safety, and digital transformation, delivered by research faculty, staff, students, and private-sector representatives. NJDOT also presented several awards recognizing research and implemented innovations.

The Research Showcase Program Agenda provides more information on the day’s proceedings, including research topics, presentation abstracts, speaker biographies, and posters. Recordings of the plenary and breakout sessions, and the presentations and posters shared during the event can also be found below.

MORNING

WELCOMING AND INTRODUCTORY REMARKS

Francis O’Connor, Commissioner, New Jersey Department of Transportation

Commissioner Jain discussed her commitment to modernizing NJDOT and how this focus area aligns with the day’s theme. NJDOT will continue research that improves how it plans, maintains, and invests in its assets, but enhance these efforts with emerging technologies. She stated that AI “has a tremendous potential to analyze complex information, identify patterns, and support better decisions,” allowing NJDOT to get ahead of challenges rather than just responding to them. The “human element” will remain important in the agency’s research, with AI used as a tool when appropriate. She recognized several recent NJDOT research projects that have adopted this approach, using new technologies to solve problems facing New Jersey’s transportation system.

KEYNOTE ADDRESS

Craig Thor, Chief Scientist at the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center, and Matt Cuddy, Operations Research Analyst at the United States Department of Transportation (USDOT) Volpe Center, gave the keynote address. They focused on how state DOTs are responding to the emergence of AI.

Craig Thor began by discussing the importance of state partners, such as NJDOT, to the FHWA’s success. He said the future of transportation—and the role of DOTs in managing transportation—is changing because of emerging technologies. To better respond to these new technologies, state DOTs need to be able to learn from each other’s research to create practical solutions.

He emphasized that the FHWA has limited jurisdiction to implement new technologies, but can provide guidance and leadership at a national level. Transportation Pooled Funds (TPFs) are being leveraged to support this goal. NJDOT is leading a new TPF to help state DOTs share their AI research and the FHWA is leading the National AI Coalition TPF to align public- and private-sector research and development nationwide. The FHWA has also established a national Super Intelligence Deployment Center of Excellence, which will compile resources from across the country to make it easier for state DOTs to learn from each other.

Debra Sabatini Hennelly, President, Resiliti

Matt Cuddy expanded more on USDOT’s Intelligent Transportation Systems (ITS) Joint Program Office. He briefly outlined the main types of AI technology—machine learning, computer vision, and large language models—before describing how transportation agencies are using them. While several state DOTs have launched pilot programs and adopted strategic plans addressing AI, there are still barriers to widespread public sector adoption, including a lack of frameworks to guide usage, difficulties in scaling usage, and workforce and agency readiness.

To help overcome these challenges, said Cuddy, the ITS Joint Program Office was launched in 2025. The integrated multimodal program has three priorities to support state DOTs: developing enabling technologies, deployment testing, and driving AI adoption. He described some of the assistance ITS provides, including guidance and knowledge-sharing on AI procurement, funding for deployment, prototype projects, and organizational assessments.

He concluded by reminding the audience that AI only works with human input, so state DOTs need to invest in workforce readiness in order to use AI responsibly and effectively. Cuddy and Thor then answered several questions from the audience on topics ranging from AI and vision zero, to addressing biased models, to using AI for infrastructure monitoring.

MORNING SESSION PANEL DISCUSSION

Pragna Shah, Manager, NJDOT, briefly introduced NJDOT’s Bureau of Research and Innovation, discussing key people and the bureau’s major functions and programs. The plenary session continued with an interactive panel discussion, “AI and the Future of Transportation,” moderated by Giri Venkiteela, Innovation Officer, NJDOT Bureau of Research and Innovation. Representatives from NJDOT, NJ TRANSIT, and the Port Authority of New York and New Jersey discussed how their organizations are adopting AI in their work. Topics included:

The panel concluded with a question on how agencies can show the return on investment for AI projects. Panelists emphasized that returns were not only monetary, but included time savings and improved efficiency.

AWARDS

The research applied vision-language models to footage from NJDOT cameras to assess congestion on state roads. One model was accurate 90 percent of the time on average. This data shows how existing cameras owned by NJDOT can be used as automatic congestion sensors, eliminating the need to install new sensors.

2026 NJDOT Research Champion Excellence Award
Recipients: Madhavi Andey, Chandrakant Patel, and Matt Gillece, NJDOT
Project: AI Assistant for Construction and Material Training and Compliance Review

2026 NJDOT Build a Better Mousetrap (BABM) Award
Recipient: Tim Bain, Communications Systems Technician 2, NJDOT Division of Operations Support & Engineering
Innovation: Gate Housing Position Indication System

Tim developed an indicator light that tells drawbridge operators whether warning gates need to be raised or lowered. By providing this information directly at the gate mechanisms, his innovation reduces confusion, errors, and communication time with remote operators. This will help operators open the warning gates more quickly, reducing delays for drivers waiting to cross.

Recipient: Kevin Krzynowek, Chief Bridge Operator, NJDOT
Innovation: Drawbridge Operations Multi-User Scheduling & Management System

Kevin was recognized for developing a cloud-based scheduling system for the state’s eleven manned drawbridges. Previously, drawbridge operators relied on paper schedules, which had caused scheduling issues and delays. With the new scheduling system, all operators and supervisors can see updates in real time, improving communication.

The Showcase featured a short video explaining both innovations.

NJDOT also took this opportunity to acknowledge two projects that received national recognition:

2026 National High Value Research Awards
Evaluation of NJDOT Hardened Traffic Paint Markings and Stripes Performance
AASHTO Honorable Mention Award in Safety, Security, and Emergencies

Research Team: Mohammad Jalayer, Subasish Das, Adam Pike, and Yusuf Mehta
Research Project Manager: Giri Venkiteela
Technical Advisory Panel: Yong Zeng and Michael Congleton

NJDOT Multi-Hazard Design of Highway Bridges Recommendations
AASHTO High Value Research Award

Research Team: Hani Nassif, Patrick Lou, Harry Capers, Jr., and Chaekuk Na
Research Project Manager: Devyn Cordero
Technical Advisory Panel: Xiaohua “Hannah” Cheng

PRESENTATION OF AWARDS

AFTERNOON

POSTERS

(click images for PDF)

Evaluating CAV Impacts on Signalized Intersection Work Zone Capacity: A New Jersey Case Study

Full-Scale Accelerated Pavement Testing of a Plastic-Modified Asphalt Mixture

Performance Assessment of Composite Plastic Modified and Conventional Polymer Modified Asphalt Mixtures Using the Wet Mixing Method

From Crash Data to Action: An Agentic AI Framework for Segment-Level Crash Frequency Prediction

Autonomous Systems Validation Platform: A Scaled Urban Testbed for Networked Decision-Making

Transforming Existing NJDOT Traffic Cameras into Autonomous Congestion Detection Systems by Utilizing Video Vision-Language Models