Author Archive: Matt Bond

NJ STIC 2024 3rd Triannual Meeting

Posted on

The NJ State Transportation Innovation Council (NJ STIC) virtually convened for its 3rd Triannual Meeting of 2024 on December 18, 2024. The meeting provided an opportunity for attendees to learn from the Core Innovation Area (CIA) Teams about their progress towards Every Day Counts Round 7 (EDC-7) goals and to view a featured presentation on the Safe System Approach (SSA) from the NJDOT Bureau of Safety, Bicycle and Pedestrian Program’s Jeevanjot Singh.

Welcome Remarks

Eric Powers, Assistant Commissioner of NJDOT Statewide Planning, Safety & Capital Investment, greeted those in attendance and opened the third and final Triannual Meeting of 2024. Mr. Powers shared his excitement for the featured presentation on the SSA as a necessary step towards increased safety on New Jersey roadways. He noted that the presentation aligns well with the recently published Complete Streets Policy, released by NJDOT in October. He reminded those in attendance that safety will continue to be a critical component and focus for the department going forward.

FHWA Updates

Christopher Paige, Innovation Coordinator and Community Planner at the FHWA NJ Division Office, provided FHWA updates and thanked the CIA Teams for submitting their EDC-7 progress reports on time. Mr. Paige announced the “Call for Ideas” for Round 8 of the Every Day Counts Initiative (EDC-8) that will run through February 4, 2025. Those interested in submitting market-ready innovations to deploy in 2026 as a part of EDC-8 can learn more here. Additionally, Mr. Paige reminded the audience that STIC incentive applications are open for Year 2025 and encouraged prospective applicants to send in a description of the proposed work, a project schedule, and a budget by July 1, 2025. Those interested in learning more about the application process for STIC funding should check out the NJDOT Tech Transfer’s STIC Incentive Funding Grant webpage

Core Innovation Areas (CIA) Updates

The Core Innovation Area (CIA) Team leaders shared updates on their progress toward achieving the deployment goals for their respective innovation initiatives. CIA Team leaders from the NJDOT and FHWA discussed EDC-7 initiatives under the five CIA Teams: Safety, Planning and Environment, Infrastructure Preservation, Mobility and Operations, and Organizational Support and Improvement. Each team’s presentation detailed their ongoing projects and outlined implementation activities, accomplishments, and challenges experienced so far in meeting the deployment goals for the innovations. A brief overview of team updates is included below:

Planning and Environment

GHG Emissions Reductions Strategies. The Planning and Environment CIA Team established GHG targets for 2024 to support New Jersey’s carbon reduction goals. To achieve these emissions targets, the Team has collaborated with MPOs, NJ TRANSIT, and PATH, ensuring the alignment of strategies between various transportation stakeholders. The Team also plans to develop and implement a ranking system for carbon reduction projects based on GHG emissions impacts, enabling NJDOT to prioritize projects that best contribute to state objectives.

Safety

Status of Pedestrian Scale Lighting Research and Resource project

Pedestrian Scale Lighting Research and Resource. The Safety Team at NJDOT collaborated with the Alan M. Voorhees Transportation Center at Rutgers University and Rowan University to develop best practices for pedestrian lighting solutions. The team is finalizing a pedestrian-scale lighting resource that includes information on types of lighting, luminaire placement, strategies to reduce fatalities and serious injuries, collaborations with utility companies, and environmental considerations. The team expects to complete the project soon.

Nighttime Visibility for Safety. The Safety Team also updated the STIC on progress made toward the installation of retroreflective tape on the backplates of intersection lights and signage. Subject matter experts are currently reviewing the finished draft, which outlines traffic signal poles and mast arm details for signalized intersection installations. At the same time, the Division of Traffic Engineering continues to install retroreflective tape on backplates where and when feasible. The Team also announced that FHWA will host a lighting training on January 28 and 29.

Infrastructure Preservation

Enhancing Performance with Internally Cured Concrete (EPIC2). The Infrastructure Preservation Team has secured a STIC Incentive Program grant to purchase specialized testing equipment, train NJDOT staff, and hire a third-party lab to conduct tests for the EDC-7 EPIC2 innovation project. The project’s next major initiative is the implementation of New Jersey’s first high-performance concrete (HPC) bridge deck at North Munn Avenue over Route 280 in East Orange, which received funding in October 2024. Construction on the bridge project is scheduled to begin in Fall 2026. The Team is also preparing final design submissions for additional candidate bridges and scoping other potential projects. In the coming quarter, they will continue collaborating with concrete suppliers, purchasing new testing equipment, and updating the High-Performance Internal Curing (HPIC) specifications to incorporate centrifuge apparatus elements. NJDOT plans to host an EPIC2 workshop in April 2025 to further advance the project.

Graphic demonstrating the difference between conventionally cured concrete and internally cured concrete

Additionally, the Federal Highway Administration (FHWA) recently released a publication on the early implementation of UHPC overlays, which will contribute to ongoing efforts in concrete innovation. The Team is working to expand the use of internally cured HPC in New Jersey and potentially New York, aligning with best practices in the concrete industry. The FHWA publication mentioned during the presentation is available here: “Experiences from Early Implementations of UHPC overlaps”.

Environmental Product Declarations (EPDs) for Sustainable Project Delivery.  The Infrastructure Preservation Team has also made progress on phase 1 of their EPDs project goals. Since August, the Team has coordinated with the New Jersey Asphalt Paving Association for guidance to create an EPD for Bottom Rich Base Course or BRBC mix asphalt.

The Team has also researched and collaborated with other state DOTs including PennDOT and DelDOT to learn how these agencies will implement EPDs. PennDOT will begin data collection in 2025 and institutionalize EPDs by 2028. While DelDOT seeks to reduce carbon in construction materials as a part of the statewide climate solutions initiative. The agency plans to achieve this through incentives and disincentives. Both agencies selected asphalt as the first material for EPDs and are working with their state’s asphalt paving association to ensure industry support. In the upcoming quarter, the CIA team will analyze data benchmarks and collaborate with industry partners to advance the adoption of EPDs.

Mobility and Operations

Weather Savvy. The Mobility and Operations CIA Team has made progress on multiple projects over the last quarter. In collaboration with researchers from NJIT, the team advanced the Weather Savvy pilot, which installs weather sensors, laptops, and cameras in vehicles to receive improved data during winter weather events. The project that started in December 2023 has expanded from 24 vehicles to 45 vehicles, and the team has decided to prioritize installations in plows because they stay on the road during weather events. The team indicated that sensors and hardware are now being installed in a junction box, which is a more secure location that will keep water, dirt and salt away from the technology.

Data gathered from cameras, remote traffic microwave sensors, and in-pavement micro radar sensors (a.k.a., Pucks) at the pilot parking location are visible on a NJIT web portal.

Truck Parking Pilot. The team was excited to share its progress toward installing portable Direct Messaging Systems (DMS) signs five miles away from the pilot Harding parking lot located on I-287 and I-78. These signs will inform drivers prior to arrival about parking availability. The team may expand the pilot project to Knowlton in 2025.

Drivewyze Alerts. Mobility and Operations updated the STIC on the Drivewyze alerts effort, a program purchased through ETC to notify commercial vehicle drivers of sudden slowdowns, congestion, and static warnings. To evaluate the efficacy and accuracy of these alerts, the CIA Team deployed NJIT researchers to drive two different loops along frequently congested highways. While results showed that static alerts were 95% accurate, drivers did not receive any congestion-related alerts. Discussions with a Drivewyze representative revealed that congestion had not been selected as an alert type among the program settings. The representative assured the issue would be corrected, and NJDOT plans to conduct another test in the near future.

EDC-7 Next Generation TIM. Finally, Mobility & Operations announced that a newly written article, “NJDOT Deploys Advance Warning Messages for Truck Drivers” will be submitted to FHWA HQ as a potential EDC innovation spotlight.

Organizational Support and Improvement

Contractor Compliance Unit Collaboration. The Team announced that the FHWA funding approved in November 2024, would support hiring a consultant in-house to advance NJDOT’s EDC-7 Strategic Workforce Development efforts. The Team also highlighted its recent collaboration efforts with the Office of Federal Contract Compliance to identify best practices for larger-scale projects. These efforts aim to design effective informational sessions on workforce development, training, and collaborating with unions. On December 2, 2024, the Team participated in an industry meeting where attendees discussed concerns about declining union membership and involvement in apprenticeship programs, trends tied to an aging labor market. Participants brainstormed strategies to increase membership and strengthen engagement. The next meeting, scheduled for January 2025, will prioritize including more unions in the discussions to enhance collaboration and address these challenges.

Feature Presentation: Safe Systems Approach in New Jersey

Jeevanjot Singh, Safety Program Manager at the NJDOT Bureau of Safety, Bicycle, and Pedestrian Programs delivered a feature presentation highlighting the practicality and necessity of incorporating the safe systems approach into New Jersey roadways.

The Safe System Approach is built on five key elements and six guiding principles:

Five Elements

  1. Safe Road Users
  2. Safe Vehicles
  3. Safe Speeds
  4. Safe Roads
  5. Post-Crash Care

Six Guiding Principles

  1. Death and serious injury are unacceptable
  2. Humans will make mistakes, so systems should be designed to accommodate and reduce harm
  3. Humans are vulnerable, and roadways should be designed to minimize kinetic energy transfer in crashes
  4. Responsibility for safety is shared among all stakeholders
  5. Safety is proactive and it is imperative to use risk-based mitigation measures
  6. Redundancy is key, with systems designed to support each other and prevent fatalities if one safety effort fails

Ms. Singh urged all attendees to reflect on the importance of Safe Systems Approach (SSA) and the responsibility that we as planners and engineers share in this effort. This responsibility is urgent, as New Jersey recorded over 600 fatalities on its roadways in 2024, far exceeding the FHWA’s 2024 target of 494 fatalities. Achieving the goal of zero fatalities by 2025 will require intentional, sustained efforts to reduce fatalities. She noted that NJDOT is making significant strides in implementing the Safe System Approach, including the updated statewide Complete Streets Plan, efforts to improve of dangerous intersections through safer engineering, and educational awareness programs, among other initiatives. While the list of efforts is extensive, she highlighted two recently implemented programs which are outlined below.

Plan for the NJDOT Route 129 SSA project in Mercer County

One example is the Wrong Way Driving Detection System. NJDOT conducted a systemic analysis to identify ramps with a high risk of wrong-way driving incidents and subsequently installed a system of dynamic flashing warning lights activated by wrong way drivers. This system was paired with additional signage and pavement markings. During the presentation, Ms. Singh shared a video that demonstrated the successful prevention of a wrong-way incident using this approach. Another example of SSA implementation by NJDOT is the Route 129 project in Mercer County, which includes pedestrian and bike safety improvements, traffic calming measures such as chicanes and raised crosswalks, and autonomous crosswalk detection warning lights that activate when a pedestrian enters the road. Although still in the early stages of development, Singh sees potential in expanding the project to include other corridors.

She concluded her presentation by outlining resources from FHWA that can support STIC members in learning more about the Safe Systems Approach. She highlighted the Safe System Road Design Hierarchy, a tool that guides road design through a four-tier decision-making system, and the Safe System Project-Based Alignment Framework, which offers another decision-making system tool for designers. She also announced that a multi-agency collaboration will host an upcoming SSA workshop, providing professionals with an opportunity to learn more about SSA alignment in New Jersey.

Announcements and Reminders

NJ Transportation Ideas Portal. Dr. Venkiteela encouraged attendees to participate in the NJ Transportation Ideas Portal, which invites public submissions of future research ideas and implementation studies. The Innovation Advisory Team evaluates these proposals for feasibility and potential future actions. He highlighted that the portal continuously accepts new research and innovation ideas for consideration for future collaborative efforts and investments. The deadline to submit research ideas for the next round of funding is December 31, 2024.

EDC-8 “Call for Ideas.” Dr. Venkiteela reminded attendees that the deadline to submit ideas for EDC-8 is February 4, 2025. FHWA is seeking suggestions for market-ready innovations to deploy in 2026. Learn more here.

NJDOT Low-Carbon Material Transportation Grant Program. In November 2024, NJDOT secured a $27.85 million grant for the Low-Carbon Transportation Material (LCTM) Program. BRIIT will lead the program’s implementation from 2025 through 2031. Dr. Venkiteela congratulated all those who contributed to the successful application.

Next Meeting. Dr. Venkiteela reminded attendees that the 1st Triannual Meeting of 2025 will occur on April 30, 2025 at 10 a.m., featuring a presentation from the Infrastructure Presentation Team.

Acknowledgment: Dr. Venkiteela concluded the meeting by thanking Amanda Gendek for establishing a solid foundation for STIC meetings in her previous role as BRIIT Manager. Dr. Venkiteela also thanked current BRIIT Manager Pragna Shah for her continued guidance and support.

A recording of the NJ STIC 2024 3rd Triannual Meeting meeting is available here. The day’s presentations can be found here and, in the sections, below.

NJ STIC 2024 3rd Triannual Meeting
Welcome Remarks & FHWA Updates
CIA Team Update: Safety
CIA Team Update: Infrastructure Preservation
CIA Team Update: Organizational Support & Improvement
CIA Team Update: Planning & Environment
CIA Team Update: Mobility & Safety
Featured Presentation: Safe System Approach in New Jersey
Reminders and Announcements

Interview with 2024 Research Showcase “Outstanding University Student in Transportation Research Award” Winner

Posted on

Traffic safety and mobility, two critical areas in transportation engineering, both require the collection and analysis of large data sets to produce proactive and comprehensive solutions. Transportation engineers have started to increasingly focus on using innovative technologies to efficiently and effectively process this data.

We had the opportunity to speak with Dr. Deep Patel, a former Ph.D. candidate and research fellow at Rowan University, whose work is at the forefront of this mission. Recently, Patel received the NJDOT Outstanding University Student Research Award for his contributions to transportation research. In this interview, Patel shares insights from his research journey, including his work on the Real-Time Traffic Signal Performance Measurement Study and the development and implementation of machine learning tools to predict high-risk intersections. His dedication to improving traffic operations and safety, along with his new industry role as a Traffic Safety and Mobility Specialist, highlights the significant impact of combining academic research with practical industry applications.

Q. Could you tell us about your educational and research experience and how you became a PhD candidate and research fellow at Rowan University?

A. First of all, thank you for your time and for considering me for the opportunity to be interviewed about my recent NJDOT award. I would also like to thank the NJDOT review committee members and my Ph.D. advisor Dr. Mohammad Jalayer, who supported me in receiving this award.

I started my master’s study in 2018 as a civil engineering student without a research focus. Then, during my first semester, I took a course called Transportation Engineering with Dr. Mohammad Jalayer. When he sought traffic counting assistance for a traffic analysis project, I eagerly joined him, becoming his first research student.

Deep Patel conducting roadside research. Courtesy of Deep Patel.

Through that experience, I started thinking about what could streamline the traffic counting process and the various uses for the data we collected. I went on to work on several research projects with Dr. Jalayer, both funded and non-funded, where we had frequent discussions, and I would present my ideas to him. Eventually, he asked me to join him as a researcher and to continue my master’s work with a research focus, which I did for two years. When he suggested I continue my studies to earn a Ph.D., I was initially surprised, but I decided to go for it since I had a lot of ideas for future research projects.

At the end of my master’s study, I began Phase One work for a Real-Time Traffic System Performance Measure Study led by Dr. Peter Jin, Dr. Thomas Brennan, and Dr. Jalayer. This project connected me with a team from Rutgers, TCNJ, and a few professionals from NJDOT and other industry folks. I represented Rowan’s end for this project, where our focus was on understanding the safety aspects including safety parameters and performance and how we could assist NJDOT transform this new technology to help save lives. For the first phase of the project, we worked on understanding the traffic signal system performance measures, and what had been adopted by other DOTs. My experience on this project drove me to pursue more research and to expand my knowledge in traffic safety.

Q. You worked on Phase One through Three of this Real-Time Traffic Signal Performance Measurement Study. What part of this project interested you the most?

A. My main takeaway from this project focused on learning more about how the transportation industry looks towards the research outputs and outcomes from the university teams. It is very interesting to understand how university-based research is being adapted for industry acceptance. Additionally, I learned what problem-solving features the industry looks for from the research component.

From a technical aspect, I learned how New Jersey signals can be enhanced and how we can optimize the performance of these signals and achieve cost savings. Let’s say you have a scenario where there is no vehicle at an intersection; how can we provide recommendations to change the signal to a red light and give the other side of the intersection a green light? So, we gathered several components in terms of mobility, safety, and economic parameters from the study that can help enhance our traffic signals in New Jersey, sharing this information with the NJDOT team.

Figure 1: An Example real-time performance monitoring on County Road 541 and Irwick Road, Burlington County, NJ
Example of real-time performance monitoring on County Road 541 and Irwick Road, Burlington County, NJ

Q. How did you see your role on the research project develop as you moved from the earlier phases to the latest phase?

A. In the first phase, we completed a comprehensive literature review to understand what is happening across the nation, which systems are being adapted, what are the best systems for providing traffic signal safety performance measures, and what are the kind of performance measures that can be adapted in an industry setting. In Phase Two, the team focused on developing mechanisms and performance measures aligned with NJDOT’s existing data, including deploying the Automated Traffic Signal Performance Measures (ATSPM) system to enhance traffic signal monitoring and optimization. To guide these efforts, an adaptability checklist was created to benchmark practices from other states and identify strategies that could be adapted to benefit NJDOT’s operations. Building on this foundation, Phase Three advanced to the demonstration and application of dashboards and performance measures, providing actionable recommendations to NJDOT on enhancing mobility and safety across various regions and corridors. These efforts aimed to save time and lives, while the integration of connected vehicle (CV) technologies remains a key focus for future work, ensuring NJDOT’s leadership in traffic management innovation.

Q. What were the specific corridors that you worked on?

A. We started with seven/eight intersections on U.S. 1. Then, we explored the whole corridor of U.S. 1 as part of Phase Three, and we also brought in Route 18, Route 130, and other intersections during this phase.

Q. Did you discover any particular surprising or noteworthy findings from this research?

A. This was a long project, extending from 2019-2024. As a result, each year we discovered new findings, and new components were often added to the project. For example, we added a CV systems component as part of the Phase Two and Phase Three projects to start planning for the future and understand what kind of data could be received and sent from CV technologies. The main benefit from this project is that it not only established current problem-solving measures but also looked into the future, helping to better understand what’s coming and how we can best face anticipated challenges that we need to start integrating at this moment. I find the combination of the present and future integration of systems and technologies interesting and important from the findings.

Q. What kind of impact do you think you and your research will have on NJDOT traffic operations and traffic safety, especially with your role now working in the industry?

A. With my previous experience as part of a university-led research team and now as a Traffic Safety and Specialist in the private sector, I am better positioned to facilitate the efficient and effective implementation of research findings.  A key factor enabling this transition is that Kelly McVeigh, who supervised the original research project, also oversees the current work that our firm is doing for NJDOT. Being on the industry side allows me to introduce and operationalize new ideas more rapidly, compared to the academic research side. This streamlined approach ensures that innovative performance measures can be deployed more quickly, and even a small modification has the potential to save lives, underscoring the value of this work.

Q. Moving to a different topic, your research frequently incorporates Machine Learning (ML) and Artificial Intelligence (AI) aspects. In your experience, what benefits does AI contribute to transportation research?

A. Over the past few years, AI and ML have undergone drastic modifications and growing levels of industry acceptance. Additionally, in research outcomes, AI and ML have played a key role in enhancing and providing new methodologies and new ways of problem-solving. As an engineer, the first thing we have to do is understand how we can solve an existing problem, and how fast, effectively, and efficiently we can do it.

Transportation is now highly reliant on big data and intensive analysis, so AI and ML back up the processing of this data, coming up with meaningful outcomes and enhancing solution measures much quickly than previous methods. In 2012 or 2013, a standard engineer would need to sit down to do a traffic study and go through manual counting, then process the data, then come up with solutions, which takes much longer to solve a problem. The problem may even change during the months-long process of developing a solution.

In traffic safety, we cannot wait for the four to five months it could take to solve a problem due to the pressing safety implications of doing so. Thus, we must start implementing countermeasures swiftly, and AI and ML components help us to quickly process data with more effective and efficient results.

During my early days as a student researcher, I would stand on the roadside, manually counting the vehicles and pedestrians to collect data for traffic studies. However. during my doctoral research, I developed my AI-driven tools that utilize advanced video systems for detection and analysis. This proactive approach enables the identification of intersections prone to high-crash scenarios well before crashes occur, allowing for timely interventions. By integrating AI and ML, my research introduced innovative methodologies for crash prediction and prevention, showcasing the feasibility of data-driven solutions to enhance roadway safety.

There is a certain chaos in human beings’ lives and surroundings that requires transportation to be a multidisciplinary field, which includes human-focused aspects. For some parts, AI is definitely required, but with other parts, we need to go through different approaches.

Q. Do you think that because of AI’s data collection and analysis possibilities, almost all engineers in the near future will need to start incorporating AI into their research?

A. It really depends. For our part of traffic engineering, very specifically, I would say yes, it would be one of the major requirements that an engineer would need to adopt. But if I was a traffic engineer working on policy or equity measures there might be some concerns related to data sharing or data privacy issues that might restrict them.

It depends on what side you are focusing on. When it comes to data collection, I would say AI incorporation is a must to collect and process data faster and more efficiently. But in terms of developing policies, rules, or statutes, there are certain psychological aspects that need to be in the thought process. Knowing human concerns and people’s approaches requires an emotional touch, which AI still lacks.

Transportation is a field connected with multiple disciplines; it touches on people’s emotions. For example, on a day when traffic does not work well when you’re returning home, you can get frustrated, and that frustration can end up in a fatal crash. There is a certain chaos in human beings’ lives and surroundings that requires transportation to be a multidisciplinary field, which includes human-focused aspects. For some parts, AI is definitely required, but with other parts, we need to go through different approaches.

Q. Congratulations on your recently approved dissertation. Could you give us some quick highlights of the research methods that went into producing your dissertation, “A Comprehensive ML and AI Framework for Intersection Safety”? What are the most important takeaways from your dissertation?

Deep Patel presenting his poster at the 2022 NJDOT Research Showcase Poster Session. Click image for PDF of the poster.

A. New Jersey is home to some of the most dangerous intersections in the United States, with four intersections ranked among the top 15 most dangerous, including the 1st, 2nd, and 3rd positions. Since 2019, there has been a trend of steadily increasing intersection-related crashes and correlated crashes within intersection boundaries. This prompted me to ask, “Why do we need to wait for crashes to happen to address the problem?”

To tackle this issue, I developed a proactive approach inspired by my work on the NJDOT research project. The approach focuses on analyzing near-miss incidents and traffic violations, using the concept of surrogate safety measures to identify potential risks before crashes occur. Surrogate safety measures help us detect near-miss events and violations, offering a predictive understanding of high-risk scenarios at intersections.

Using AI and ML, we developed tools that analyze vehicle and pedestrian trajectories in detail. These tools detect and classify conflicts, such as left-turn conflicts or yielding conflicts, enabling us to predict potential crash scenarios based on behavioral patterns at intersections. This proactive analysis allows us to recommend design changes and interventions before crashes occur.

Then, we explored the noncompliance component in a certain area, like red light violations or jaywalking. For instance, our analysis revealed that one in every four pedestrians does not use crosswalks. By integrating historical crash data, proactive trajectory analysis, and noncompliance trends, we developed a tool that ranks intersections based on multiple criteria. These include potential high-crash scenarios, contributing factors, and the economic impact of injury severity at specific locations.

Determining Key Factors Linked to Injury Severity in Intersection-Related Crashes in NJ. Deep Patel, Rowan University (2023 Research Showcase). Click image for slides.

Additionally, the research explored how emerging technologies, such as connected and autonomous vehicles, could be adapted to enhance intersection safety. By conducting trajectory analyses, we assessed how data from these technologies could inform future safety measures and interventions.

Overall, my research focused on identifying key factors within intersection boundaries to reduce crashes, improve mobility, and do so in a cost-effective manner. This comprehensive approach combines proactive analysis, advanced technologies, and human behavior insights to deliver practical and impactful solutions for roadway safety.

Q. So this tool seems to be one of the most important takeaways. Is the tool ready for NJDOT use to identify potential high crash risk intersections? Is that the main intent of the tool?

A. Yes, exactly. The tool is ready but not yet publicly available. We tested it on several intersections. It is currently a proprietary tool of my professor and myself at Rowan University. Anyone interested in using the tool can connect with us, but it is not yet publicly available and certain permissions are required.

Q. Is NJDOT using it or can they use it?

A. No, the department is not using it because this was part of my recent defense. They are aware of the tool’s capabilities because it was part of an innovative showcase. The tool’s documentation has been published through the University Transportation Center (UTC). Hopefully, in the near future, it could be applied by NJDOT.

Q. Looking ahead, you have your new position in an industry role. Would you like to continue with this sort of focus on transportation research, or are you anticipating a different career direction?

A. With my new position as a Traffic Safety and Mobility Specialist, I will be focused on transportation research, conducting high-quality industry research where I would help develop safety and mobility performance measures on certain corridors designed to move traffic more effectively and enhance safety on the roadways. My work will also include industry deployment and understanding the agencies’ concerns regarding the challenges they face.

Looking ahead, I see my career direction as a blend of research and practical implementation, ensuring that innovative solutions are not just developed but also applied to make a real-world impact. Ultimately, if my work can contribute to saving even a single life, I will consider it a meaningful and worthwhile achievement.

Resources

Jin, P. J., Zhang, T., Brennan Jr, T. M., & Jalayer, M. (2019). Real-Time Signal Performance Measurement (RT-SPM) (No. FHWA NJ-2019-002).  Retrieved at: https://www.njdottechtransfer.net/wp-content/uploads/2020/01/FHWA-NJ-2019-002.pdf

Jin, P. J., Zhang, T., Brennan Jr, T. M., & Jalayer, M. (2019). Real-Time Signal Performance Measurement Phase II. Retrieved at:  https://www.njdottechtransfer.net/wp-content/uploads/2022/08/FHWA-NJ-2022-002-Volume-I-.pdf

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

Patel, D. (2024). “A Comprehensive ML and AI Framework for Intersection Safety: Assessing Contributing Factors, Surrogate Safety Measures, Non-Compliance Behaviors, and Cost-Inclusive Methodology.” Theses and Dissertations. 3305. https://rdw.rowan.edu/etd/3305

For more information about the 26th annual NJDOT Research Showcase, visit: Recap: 26th Annual NJDOT Research Showcase

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

Posted on

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

Posted on

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

Posted on

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

Posted on

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.