Source: UHPC SOLUTIONS North America (top left); Midwest Roadside Safety Pool Fund (bottom left); Colorado Department of Transportation (right)

NJDOT’s Involvement with Transportation Pooled Fund Program

For over 45 years, the Transportation Pooled Fund (TPF) Program has made it possible for public and private entities to combine resources for high‑priority transportation research. By pooling funds and expertise, participating organizations can support research that can lead to innovative solutions at a lower cost to agencies and extend the reach of their research budgets.

State DOTs often fund TPF Program studies using State Planning and Research (SP&R) funds, which can be applied to transportation studies as well as research, development, and technology (RD&T) transfer activities.

We spoke with Dr. Giri Venkiteela, Innovation Officer in the Bureau of Research, Innovation and Information Transfer (BRIIT), to learn about NJDOT’s recent involvement with the Transportation Pooled Funded Program.


Q. What is the primary goal of Transportation Pooled Fund (TPF) Program?

The Federal Highway Administration leads the Transportation Pooled Fund Program

A. The Transportation Pooled Fund Program, or TPF, makes it possible for state DOTs, the Federal Highway Administration (FHWA), and other organizations to partner when there is a shared interest in solving a transportation-related problem. Partners contribute funds and other resources to cost-effectively address problems through research, planning, and technology transfer activities.

The FHWA administers the TPF Program. Only the FHWA or a State DOTs may initiate and lead a pooled fund study. Local and regional transportation agencies, private industry, foundations, and institutes of higher education can partner with sponsoring agencies to conduct pooled fund projects.

Q. What is your involvement with the TPF Program?

A. I work in NJDOT’s Bureau of Research, Innovation and Information Transfer (BRIIT) and serve as the Transportation Pooled Fund Program’s project manager, or coordinator on behalf of NJDOT. Among my responsibilities, I disseminate information about new “open” solicitations for projects from sponsoring agencies to NJDOT’s subject matter experts (SMEs) to gauge their interest in participation. Sometimes NJDOT SMEs or our customers — who network with their peers at other agencies — will hear about an upcoming or worthwhile project and ask that I monitor its status so that NJDOT can join as a partner once the project is posted. Depending on the topic, I may also serve as the agency’s SME on a particular project.

Q. How does NJDOT select project topics from “open solicitations” to join through the TPF Program?

A. The FHWA pooled funded website is publicly available and anyone can view the many “open solicitations” for projects that seek funding. We have a research budget that can and does support participation in pooled funded studies, but we also must set-aside funds and commit to the projects we join for several years over the lifetime of the research. Our budget is not a static number but dynamic. The amount that we can commit depends on how many projects NJDOT is interested in joining.

BRIIT’s Research Manager works with leadership in departmental units seeking funding to ascertain the value potential of individual projects and I offer my advice during this process as a member of BRIIT.

Q. How do NJDOT staff participate in these studies, and what are the requirements for participation?

A. The NJDOT unit managers need to assign an SME for the research project study. I serve as the research program manager but we need to have an SME who is interested in being the participant. I coordinate with FHWA on our financial commitment and make sure the FHWA website is up-to-date with our participation.

Once the project receives the necessary financial commitments, the lead state is responsible for the administration of the research project, which may include the selection of universities or contractors to perform the research.

Once we all contribute the money, the project proceeds like a regular research project. The lead state holds quarterly meetings, prepares quarterly progress reports and disseminates the research. They keep the various participating agencies informed of progress. The lead state uploads progress reports to the FHWA’s website and the states will have their own websites to share project reports, latest news and other tools.

If SMEs or other researchers want to know what’s going on in any particular quarter, they can find the information that is shared. Our SMEs may also be involved in the development of a scope of work and, over the course of the project, may have specific needs that they would like for the selected research team to address — for example, such as thorough testing of materials.

Q. What are some examples of successful pooled funded studies and their outcomes that NJDOT has joined?

Researchers at Midwest Roadside Safety Facility state-of-the-art computer software, including LS-DYNA, to simulate real-life impact events. Using computer simulation, it is possible to reduce design costs and better understand system behavior. Click for examples.

A. The Midwest Roadside Safety Pool Fund program is a fantastic pooled fund study where a lot of crash testing of roadside barriers with different materials has been performed. The costs for such testing would be difficult for one state to bear so it makes sense for the states to come together so that more testing can be done. In this case, Nebraska DOT leads the research. Back in 1990, three Midwestern states started this pooled funded research effort, but it has grown to now include 22 lead and partnering states. The participating state DOTs collaborate with the Midwest Roadside Safety Facility at the University of Nebraska-Lincoln. So, if our SMEs see a new design or material that needs testing, they can put this request forward through this study.

Clear Roads Winter Maintenance Research TPF-5(353), led by the Minnesota Department of Transportation, was a 2024 Recipient of the FHWA Transportation Pooled Fund Excellence Awards.

The Clear Roads Winter Highway Operation — now in its third phase — is another great example. The Clear Roads pooled fund project began in 2004 with four members interested in snow clearance and related issues. The project performs real-world testing of winter maintenance materials, methods, and equipment and has grown to include 39 participating states. The Minnesota’s DOT leads the project, and was recently recognized with a TPF 2024 Excellence Award.

This is just a handful of examples — there are many others being driven by state DOTs, each of which have their own unique flavors.

Learn more about research on and use of Ultra-High Performance Concrete. David Hawes, Resident Engineer for Pulaski Skyway, NJDOT is featured at 2:13.

I would also like to mention one non-state DOT sponsored research project. The Structural Behavior of Ultra High Performance Concrete project is led by the FHWA itself through its Turner Fairbanks Research Center. The project conducts various experiments with UHPC. Every state wants to know what is happening with this relatively new material. The project objective is to develop knowledge on the structural performance of UHPC materials in highway bridges and structures. The test results are expected to inform proposed structural design guidance for UHPC components and support usage of UHPC by interested DOTs.

Q. How are the results and findings of these studies disseminated to the participating agencies, public or other stakeholders?

A. Some projects are ongoing like the Midwest Roadside Safety study. Information is flowing through their research hub with project reports and other materials posted on their website along with information on conference presentations, trainings, and newsletters. If you need any information, it will be conveyed through the program.

But for some pooled fund projects, they need to implement some of the tools that they are developing so that is how they would come to contact the states, such as to have something tested or looked at. The first priority would be given to the states that are participating in the pooled funded study.

For FHWA, if something new comes out of the pooled funded study, I think they may elevate the innovations into other areas such as through the Every Day Counts Program.

Recently FHWA started a pooled fund excellence awards to highlight the importance of collaboration and partnership in transportation research and encourage states to participate. Actually, I participated as a judge last year. We selected two projects for the inaugural TPF Excellence Awards. I already mentioned the Clear Roads Winter Maintenance Research project. The other award was given for an Indiana DOT project, Member-Level Redundancy in Built-up Steel Members, which led to new AASHTO Guide Specifications.

Q. How do NJDOT SMEs who are participating in the pooled funded studies share what they have learned?

A. We have started to ask that the SMEs share a short yearly progress report that reflects upon what they may be learning. Since NJDOT is obligating funding, we need to have some kind of justification for the commitment. The reporting can help us consider the benefits of the research or innovations being advanced, and to consider some of its possible implications for NJDOT practices.  

With a good and continuing dialogue with our SMEs, we should be able to determine if it makes sense to have the SMEs speak at a future NJ STIC meeting to share what they are learning and convey what is innovative about the pooled funded study’s research.

Q. Do you foresee opportunities for having selected researchers from funded projects for which NJDOT was a partner share their findings with NJDOT employees such as at a Tech Talk?

A. The SMEs are well-positioned to help us to identify whether it might make sense to invite a researcher from the study to speak. They can help identify how best to promote and disseminate the research and innovation through some other activity.

Q. Has NJDOT served as the lead organization on pooled funded research? Are there projects that NJDOT would like to lead?

A. We have not led a pooled funded research project yet, although we had some initial plans to do so before the pandemic.  At this point, we think it may be more productive to join as a participating organization. We think serving as participating organization may be a cost-effective way to direct some of our funds and have our SMEs connected to meaningful research.

Of particular note, we just joined the Northeast Transportation Research Consortium (NTRC), a pooled funded study for our AASHTO Region 1, that will support peer exchange activities. The effort seeks to enhance member state collaboration in solving our common problems. This is a pooled fund initiative that is just getting launched and is led by Vermont DOT. NJDOT is one of the six participating state DOTs in the Northeast.

Q. Are there any other projects that are you are thinking of joining at this time?

A. Yes. This is an ongoing process. There are a few projects that we are considering. Solicitations can pop up throughout the year.


Resources

National Cooperative Highway Research Program. 2023. “TPF: Transportation Pooled Fund” (website). https://www.pooledfund.org/

National Cooperative Highway Research Program. 2024. “Transportation Pooled Fund – Open Solicitations” (web page). https://pooledfund.org/Browse/open

National Cooperative Highway Research Program. 2024. “Clear Roads Phase II” (web page). https://pooledfund.org/Details/Study/604

National Cooperative Highway Research Program. 2024. “Midwest Roadside Safety Pooled Fund Program” (web page). https://pooledfund.org/Details/Study/653

National Cooperative Highway Research Program. 2024. “Structural Behavior of Ultra-High Performance Concrete” (web page). https://pooledfund.org/Details/Study/695

National Cooperative Highway Research Program. 2024. “TPF: National Transportation Research Consortium (NTRC) (website). https://pooledfund.org/Details/Study/783

National Cooperative Highway Research Program. 2024. “Member-level Redundancy in Built-up Steel Member” (web page). https://pooledfund.org/Details/Study/482

AASHTO. 2018. Guide Specifications for Internal Redundancy of Mechanically Fastened Built‑Up Steel Members. Washington, DC: American Association of State Highway and Transportation Officials.

AASHTO. 2018. Guide Specifications for Analysis and Identification of Fracture Critical Members and System Redundant Members. Washington, DC: American Association of State Highway and Transportation Officials.

Did You Know? AASHTO Publications Available Electronically

The New Jersey Department of Transportation (NJDOT) Research Library has approximately 200 publications from the American Association of State Highway and Transportation Officials (AASHTO) available electronically in an internal SharePoint drive. These documents are available only to NJDOT employees and will not be found in the New Jersey State Library’s catalog.

These documents include manuals, specifications, and guidance from AASHTO and its industry partners. A current list of publications that can be accessed is here.

Additional documents are available in print and/or electronic formats from the NJDOT Research Library. There is some overlap in the electronic and print documents. For more information on the library’s AASHTO resources, please see Did You Know? AASHTO and TRID Resources – NJDOT Technology Transfer

To request any of these documents, please contact the NJDOT research librarian, Eric Schwarz, MLIS, at (609) 963-1898, or email library@dot.nj.gov. Many of the documents require a special login procedure, which will be explained when the Research Library sends the user a link to the document.

Did You Know? AI in Transportation

Artificial Intelligence (AI) is rapidly reshaping transportation by improving safety, efficiency, and sustainability across various applications. From real-time traffic monitoring to predictive infrastructure maintenance, AI is becoming a critical tool for advancing transportation systems in New Jersey and nationwide. This article covers the use of AI in transportation research and implementation, with examples from the 2024 NJDOT Research Showcase, New Jersey and other state DOTs.  


AI on Display at the 2024 Research Showcase  

NJDOT held its 2024 Research Showcase on October 23, highlighting innovative transportation research and its implementation throughout New Jersey. During the morning panel discussion, Giri Venkiteela, Innovation Officer in NJDOT’s Bureau of Research, Innovation & Information Transfer, stated that Artificial Intelligence (AI) held significant promise for producing economic and environmental advancements in transportation due to its real-time predictive capabilities and proposed that NJDOT adopt protocols that can adapt to the pace of AI. Similar insights were heard throughout the showcase, where AI emerged as a central theme across numerous presentations and discussions.

AI, encompassing subcategories like Machine Learning (ML) and Artificial Neural Networks (ANN), allows researchers to analyze and model large data sets in real-time, saving significant labor hours and producing efficient, immediate results. Throughout the showcase, various projects ranging from enhancing pedestrian safety to predicting natural disasters utilized AI-based models.  

Deep Patel received the 2024 Outstanding University Student in Transportation Research. As part of a research team at Rowan University, Patel deployed the AI model, YOLO-v5, to analyze video data from multiple New Jersey intersections, providing information on pedestrian volumes, traffic volumes, and the rate of vehicles running red lights, among other variables. The team then ranked intersection safety using the metrics analyzed by the AI model. 

Slide from Meiyin Liu’s presentation on real-time traffic flow analysis.

Patel’s research exemplifies the growing trend of integrating AI methods into traffic safety analyses, which continued into several presentations given in the afternoon Safety Breakout Sessions. Here, Rutgers professor Meiyin Liu presented her method for estimating real-time traffic flow through a combination of Unmanned Aerial Systems (UAS) and deep learning algorithms. A computer-mounted UAS would be used to record video data of a highway, which then gets transmitted to the YOLO-v5 computer vision AI that detects vehicle volume and estimates speed. This data collection method facilitates a real-time traffic flow analysis across a comprehensive geographic coverage that could enhance traffic performance and crash risk prediction. Afterward, Branislav Dimitrijevic, a member of an NJIT research team, showcased an AI-driven project that utilized LiDAR technology and YOLO-v5 computer vision to activate a Rectangular Rapid Flashing Beacon (RRFB) when pedestrians approached crosswalks, enhancing road safety.

Poster by Indira Prasad from the 2024 NJDOT Research Showcase.

Multiple posters featured at the Research Showcase contained elements of AI, including a poster titled “Integrating AI to Mitigate Climate Change in Transportation Infrastructure” made by Indira Prasad and “Artificial Intelligence Aided Railroad-Grade Crossing Vehicular Stop on Track Detection and Case Studies” highlighted by researchers at Rutgers’ CAIT. 

AI’s critical role in the maintenance and preservation of infrastructure was also evident in the afternoon’s Sustainability Breakout Sessions. Indira Prasad, a Stevens Institute of Technology graduate student, conducted a review of future innovations in sustainable and resilient infrastructure. Prasad explained how AI’s pattern recognition capabilities could be used to analyze large data pools and help forecast natural disasters, enabling a rapid response to augment existing infrastructure. Surya Teja Swarna, a Rowan University postdoctoral researcher, demonstrated an innovative approach where state DOTs could use mobile phones mounted on vehicles to record roadway surface deformations, which then would be analyzed in real-time by an AI computer vision software, drastically reducing the time and costs required for road condition assessments.


Deployment of AI in Programs and Project Implementation  

In addition to research from academic institutions, State DOTs and various other state, local and public transportation organizations have started to deploy AI-based methods and tools on various programs and projects. 

Peter Jin, a Rutgers professor, received the 2024 NJDOT Research Implementation Award for his role in the New Brunswick Innovation Hub Smart Mobility Testing Ground (Data City SMTG).  The project, created in partnership with NJDOT, the City of New Brunswick, and Middlesex County, functioned as a living laboratory for transportation data collection, containing Self-Driving Grade LiDAR sensors and computing devices across a 2.4-mile multi-modal corridor. Private and public sectors can use the data to enhance their advanced driving systems, automated vehicle models, and other AI-based projects. 

Additionally, NJDOT has established a program integrating unmanned aerial systems (UAS) into its transportation operations. UASs provide high-quality survey and data mapping information, which, when paired with AI-based technologies, can be analyzed in real time to document roadway characteristics or conduct damage assessments for natural disasters. Meiyin Liu’s real-time traffic flow assessment research is one example of how UAS can be paired with AI. 

The methods used by CAIT to detect and analyze railroad-grade crossings. Courtesy of CAIT.

The use of AI for railroad-grade crossing detection has been demonstrated on several projects in recent years.  NJ TRANSIT, the statewide transit agency, recently received a $1.6 million grant from USDOT to implement a railroad-grade crossing detection system. The system, developed in partnership with CAIT researchers, will be deployed at 50 grade crossings and aboard five light rail vehicles throughout the state. The railroad-grade crossing detection system features multiple cameras on grade crossings and light rail vehicles to record data for an AI computer vision model that monitors and analyzes grade crossing behavior such as near-miss incidents.

For a project recently completed with the Federal Railroad Administration, CAIT researchers examined “stopped-on-track” incidents, which are a leading cause of grade-crossing accidents. During the poster session at the 2024 NJDOT Research Showcase, CAIT’s researchers highlighted a detection system for identifying stopped-on-track incidents and case study examples of how the critical locations can be addressed through design or other interventions. They found that targeted intervention using the AI detection system could reduce stopped-on-track incidents by up to 86 percent.

Visual example of how LiDAR senses the surrounding environment.

Other State DOTs have also started to implement AI-based programs. The Georgia Department of Transportation, in partnership with Georgia Tech, completed a survey of 22,000 road signs around potentially dangerous road curves using AI and vehicle-mounted mobile phone cameras to improve safety at road curves. The Texas Department of Transportation (TxDOT) assessed pavement conditions using LiDAR and AI. TxDOT’s project shares similarities with the research presented by Surya Teja Swarna, but it utilized LiDAR instead of a mobile phone camera.  

In 2022, the Nevada Department of Transportation partnered with the Nevada Highway Patrol, the Regional Transportation Commission of Southern Nevada, and a private technology company to launch an AI-based platform that facilitated the reporting of real-time crash locations. A study on this project found that the AI platform uncovered 20 percent more crashes than previously reported and reduced emergency response time by nine to ten minutes on average while eliminating the need to dial for help.


Recent National Research  

Responses from state DOT officials demonstrate the varied applications of ML solutions. Courtesy of NCHRP.

The National Cooperative Highway Research Program (NCHRP) published a 2024 research report,  Implementing and Leveraging Machine Learning at State Departments of Transportation, that identifies trends in AI transportation research and implementation with a specific focus on machine learning and creates a roadmap for future implementation. The researchers surveyed State DOTs on plans regarding AI, reported case studies of ML implementation by State DOTs, and listed strategies to help DOTs facilitate further inclusion of AI solutions.

The survey of the state DOT officials covered various topics, including the transportation agency’s familiarity with AI methods and tools, types of methods and applications utilized, and challenges in implementation. Among the challenges to implementation, DOT officials noted a lack of public trust, insufficient data collection and storage infrastructure, and, most commonly, scarce labor with knowledge of AI. Most computer and data scientists choose to work in the private sector, and it can be difficult to recruit them to a transportation agency.

The NCHRP report also included multiple case studies from state DOTs such as Nebraska, California, and Iowa, documenting the experiences of these agencies in developing and implementing ML programs.

  • Nebraska DOT (NDOT) used a computer vision Convolutional Neural Network (CNN) algorithm to detect and analyze guardrail quality. NDOT recorded 1.5 million images of guardrail data and used AI to save time and money compared to the manual detection alternative. Among the challenges, NDOT observed that their agency did not have the necessary infrastructure to process large volumes of data and lacked in-house ML expertise. The agency solved the former issue by using a private vendor to process the data and the latter by collaborating with consultants from the University of Nebraska. The algorithm achieved accuracies of 97 percent for guardrail detection and 85 percent for their classification into three types. 
  • The California Department of Transportation (Caltrans) has leveraged AI/ML applications across various projects and partnered with numerous tech companies, including Google. One area of emphasis for Caltrans has been workforce capacity development. While most staff do not have experience with AI-based data analytics, they do have experience with GIS. Caltrans has worked with GIS tool developers to incorporate ML functionalities into the basic user interface of GIS programs, making it more intuitive for their workforce. 
  • Iowa State University, funded by the Iowa Department of Transportation, developed a real-time ML tool to monitor highway performance, enabling a rapid response to traffic congestion. The researchers identified the need for high-performance computing as a significant challenge preventing large-scale implementation. Mass deployment of the tools used in the research study would require a considerable expense, partially due to the stipulation that the code be at least 99 percent reliable. 

For more information on the application and implementation of AI by transportation agencies, the National Academies of Sciences, in collaboration with the NCHRP, published two additional reports in 2024. One, Artificial Intelligence Opportunities for State and Local DOTs: A Research Roadmap, utilizes machine learning methods to analyze research trends in AI and how State DOTs can implement the research. The other, Implementing Machine Learning at State Departments of Transportation: A Guide, serves as a complementary document to the NCHRP report on implementing and leveraging machine learning. 

On a national level, USDOT published its Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence compliance plan in September 2024. USDOT has taken several measures to advance the implementation of AI, including forming an AI Governance Board chaired by the Deputy Secretary and vice-chaired by a new Chief Artificial Intelligence Officer (CAIO), creating an AI Accelerator Roadmap, and providing funds for AI research and implementation.

Lastly, the American Association of State Highway and Transportation Officials (AASHTO) hosted a knowledge session examining the role of AI in transportation in April 2024. Practitioners on the panel highlighted the potential of AI in eliminating the dangerous aspects of data collection and allowing for proactive solutions rather than reactively responding to crashes or injuries.  The panelist discussion touched upon the importance of building trust in a period of rapid AI development, noting the critical role that academic researchers can play as partners with state DOTs to advance and develop the AI technology in ways beneficial for traffic safety and workforce safety, among other topics.


TRID Database 

Artificial Intelligence-based research can be found via TRB’s TRID database. The following are some relevant articles published on recent New Jersey transportation research in AI.

  • Bagheri, M., B. Bartin, and K. Ozbay. (2023). Implementing Artificial Neural Network-Based Gap Acceptance Models in the Simulation Model of a Traffic Circle in SUMO. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2677. https://trid.trb.org/View/2166547
  • Hasan, A.S., M. Jalayer, S. Das and M. Bin Kabir. (2024). Application of machine learning models and SHAP to examine crashes involving young drivers in New Jersey. International Journal of Transportation Science and Technology, Vol. 14. https://trid.trb.org/View/2162338
  • Hasan, A.S., M. Jalayer, S. Das and M. Bin Kabir. (2023). Severity model of work zone crashes in New Jersey using machine learning models. Journal of Transportation Safety & Security, Vol. 15. https://trid.trb.org/View/2190127
  • Najafi, A., Z. Amir, B. Salman, P. Sanaei, E. Lojano-Quispe, A. Maher, and R. Schaefer. (2024). A Digital Twin Framework for Bridges. ASCE International Conference on Computing in Civil Engineering 2023, American Society of Civil Engineers, pp 433-441. https://trid.trb.org/view/2329319  
  • Nayeem, M., A. Hasan, M. Jalayer. (2023). Investigation of Young Pedestrian Crashes in School Districts of New Jersey Using Machine Learning Models. International Conference on Transportation and Development 2023, American Society of Civil Engineers. https://trid.trb.org/View/2196775  
  • Patel, D., P. Hosseini, and M. Jalayer. (2024). A framework for proactive safety evaluation of intersection using surrogate safety measures and non-compliance behavior. Accident Analysis & Prevention, Vol. 192. https://trid.trb.org/View/2242428
  • Zaman, A., Z. Huang, W. Li, H. Qin, D. Kang, and X. Liu. (2023). Artificial Intelligence-Aided Grade Crossing Safety Violation Detection Methodology and a Case Study in New Jersey. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2677. https://trid.trb.org/VCiew/2169797  
  • Zaman, A., Z. Huang, W. Li, H. Qin, D. Kang, and X. Liu. (2024). Development of Railroad Trespassing Database Using Artificial Intelligence. Rutgers University, New Brunswick, Federal Railroad Administration, 80p. https://trid.trb.org/view/2341095 

Additional Resources

Broad Agency Announcement for the Accelerated Market Readiness Program

The Federal Highway Administration (FHWA) has released a new five-year open Broad Agency Announcement (BAA) for its Accelerating Market Readiness (AMR) program. The AMR program provides funding to advance and apply emerging transportation innovations that have the potential to enhance roadway safety, increase efficiency, or improve performance. The BAA, which opened on October 29, 2024, intends to advance the objectives of the AMR program by soliciting a variety of white papers and proposals that could help deliver innovative projects and practical solutions.

Acceptable research topics for the BAA include:

  • Safety
  • Shortening Project Delivery
  • Infrastructure Performance
  • Climate and Sustainability
  • Equity
  • Digital Twins and Advanced Simulation Techniques.

Awards from the BAA may be of any dollar value, but it is anticipated that most individual awards will range between $300,000 and $600,000.

To be considered for an award, offerors must submit a project white paper, which must be no more than six pages in length and comprise of a cover page and a technical approach. FHWA will work to return white paper evaluations within 60 days of receipt.

The proposed solicitation number will be 693JJ325BAA0001. The open period of the BAA is anticipated to be October 29, 2024, through October 28, 2029. The FHWA held a Virtual Industry Day on November 20, 2024 to explain the FHWA’s overall vision, to provide details regarding the AMR BAA requirements, and give interested parties the opportunity to ask questions.

For more details on the BAA, consult the BAA summary provided by the FHWA.

If you believe you have an innovation or technology that is eligible for funding under the AMR Program that New Jersey should pursue, please email Bureau.Research@dot.nj.gov.