Exploring the Future of Environmental Product Declarations at NJDOT: Q&A Interview with NJDOT’s Project Lead


Under the FHWA’s Climate Challenge, state DOTs and local agencies receive training and work with various stakeholders including those from industry and academia to implement projects that quantify the environmental impacts of pavements using Life Cycle Assessment (LCA) and Environmental Product Declarations (EPDs). EPDs provide an in-depth look at the use effects and environmental impacts of materials, processes, and mixtures. With a general goal to reduce carbon emissions, DOTs are moving towards the use of EPDs for selecting pavement uses and processes.

We spoke with Nusrat Morshed, Project Engineer in the Pavement Design & Technology Unit at NJDOT, about two FHWA project grants funded under the Climate Challenge initiative that she supervises. Both projects focus on the potential use of EPDs and LCA in New Jersey and will allow for NJDOT to develop a strong baseline understanding of EPD use.


FHWA Funded Climate Challenge Projects

Q. Can you tell us about two FHWA-funded Climate Challenge projects listed for New Jersey. How is NJDOT currently involved in these FHWA funded projects? What are tasks for these projects?

A. NJDOT applied for this research project funding in early 2023 after the advertisement was released. I had spoken with representatives from Rowan University and Rutgers University to gauge their interest in this research, and both were on board. EPDs is a very new concept and term within the transportation field. This made it challenging to determine what the project scope should be for our grant applications. We received funding from FHWA immediately, but there were some technical issues in the allocation of state and federal funding shares that we needed to sort out before we could proceed. Both research teams officially began work in September and October of 2023 and they will have until the end of 2024 to carry out the work.

The research team for Project 1, Utilization of EPDs and LCAs to Promote Sustainability in NJ’s Pavements, is led by Dr. Yusuf Mehta from Rowan University and they are teamed with the research sub-consultant, Advanced Infrastructure Design (AID). The objective of this project is to utilize EPDs and LCAs to promote consideration of sustainability in maintaining NJDOT’s pavement infrastructure. The tasks within this project scope include: conducting a literature review, defining the goals and scope of the comparative LCA analysis, data collection, and analysis of results and interpretation.

The research team for Project 2, Improve Sustainability of Asphalt Pavement Overlay in NJ, is led by Dr. Hao Wang from Rutgers University. The research project objective is to improve the sustainability of asphalt pavement overlay in New Jersey. The project’s basic tasks include: documenting experiences and lessons of using FHWA’s LCA PAVE tool based on analysis of pavement overlay project in New Jersey DOT, evaluating quantification methods for calculating carbon emissions at the use phase of pavement, providing recommendations for use of LCA in decision making of pavement overlays, and preparing a final report and presentation.

Example EPD summary, retrieved from USDOT FHWA Tech Brief: Building Blocks of Life-Cycle Thinking

I am the key point person for both projects.

Q. What is the status of these FHWA funded projects? What resources have been helpful so far?

A. Both projects are underway now but still in the early stages. I received a status report from Project 1 about a month ago and expect a status report from Project 2 before March. For both projects, the focus has been to complete a literature review. One resource that was particularly helpful was the National Asphalt Pavement Associations (NAPA) website, as they have a lot of information on EPDs — 15 EPDs thus far have been identified — which are NJDOT specifications. We have also reached and had a meeting with the Port Authority of New York and New Jersey (PANYNJ) to get information on their own EPD process.

Q. The FHWA Climate Challenge program seems like it has established an approach to promote knowledge sharing and fostering a community of practice. Can you tell us about it?
A. Every quarter, FHWA conducts a climate challenge webinar, and on this webinar there is usually a featured presentation from an expert and then brief update presentations from climate challenge project teams. These project teams extend beyond New Jersey, so other states can hear how NJDOT is doing with these projects and we can learn from our peers in other states.

Previously our updates have been limited to 2 or 3 slides, however, later this spring I will have two reports to base our presentation upon, which will be more comprehensive and reflective of NJDOT’s progress.

Attendees at a Climate Challenge Training session. source: FHWA

The quarterly webinars have been helpful and instructive. EPDs and sustainable resiliency are also very hot topics and several other resources are emerging that we can reference. For example, there was an entire session on EPDs at the Transportation Research Board’s Annual Meeting. Published literature has also been very helpful.

As a part of the project grants, FHWA is providing EPD-specific trainings. Both research teams and I have brainstormed about trainings our teams require. I have coordinated with FHWA as a Climate Challenge member and explained our training needs for accomplishing these two projects. FHWA and I drafted an agenda based on these research needs and we have scheduled a day and a half in-person training for March 2024. I requested that both of my teams submit their findings, as a status report before that training. So it also will be our official first status meeting for both project teams.

As a Project Engineer overseeing these projects, I am not able to work directly with the research, but I provide guidance to the universities and have been the communication bridge between them and FHWA. The training is hosted by FHWA and conducted by FHWA and a third-party organization that specializes in EPDs. These trainings are hosted throughout the U.S. To make this happen, FHWA provided us with their schedule, and we negotiated a time for them to do the training in March 2024.

Q. Who was in attendance for this training?

The training was done on March 12-13, 2024 at NJDOT. This training was focused on team members from both projects. There were representatives from the NJDOT Bureau of Materials, NJDOT Bureau of Statewide Strategies and NJDOT Division of Environmental Resources who participated.

Q. How has this funding assisted with NJDOT’s Every Day Counts (EDC) EPDs related goal?

Unless a NJ STIC Incentive Grant is awarded, FHWA does not provide any funding directly for advancing the EDC-7 innovation, but instead supports the deployment goals through the mobilization of FHWA resource specialists or subject matter experts who are farther along with innovation’s deployment. Luckily, the research of EPDs is a goal within EDC-7, so both of the funded Climate Challenge projects are indirectly supporting that EDC-7 goal.

Q. Have any pilot programs begun?

As we are still in the research stage for EPD use, we have not created any pilot programs yet.

Environmental Product Declarations in the Future

Q. Can you describe the status and implementation goal for NJDOT’s EDC-7 goal for advancing EPDs in New Jersey?

NJDOT’s EDC-7 goal for advancing EPDs in New Jersey is still in the preliminary stages of information gathering. Both of these climate challenge projects will assist with building up a robust set of literature that is necessary for next steps. Our goal is to get ideas for future recommendations. As of now, I would say we need to identify a few plants or suppliers and get some real-time data for different types of considerations based on research needs. Then we need to identify which way we can achieve EPD targets like lowering carbon emissions.

The stages of Pavement’s life cycle. Retrieved from USDOT FHWA Sustainable Pavements Program.

Q. What challenges, if any, has NJDOT faced while working to incorporate EPDs into pavement considerations?

EPD is based on many stages, which require their own literature review. For example, a product category rule, or a set of rules for measuring life-cycle analysis must be developed first. EPDs have different stages that all must be measured — specifically, the production stage, transportation stage and construction stage, or as they are called the A1, A2, A3. Achieving the goal of reduction in carbon emissions through EPDs requires a lot of research and literature review, and it will not be easy to get all the needed information, even when speaking with experts. Starting from scratch, the ability to quantify an EPD could take at least two years. So, it’s not that you will be getting something very quickly. We are just exploring now what is out there and how we can think about something in terms of New Jersey’s pavement mixes.

Q. How does NJDOT use or reference the published EPDs in New Jersey as reported by the National Asphalt Pavement Association’s Emerald Eco-Label tool?

We have looked at the National Asphalt Pavement Association (NAPA) website and reviewed their own PAVERS tool. It has been helpful to see how they do life-cycle analysis. They have their own LCA tool and we use the FHWA LCA tool — so there will most likely be differences. The FHWA LCA tool is expected to be updated soon.

Q. Do you foresee NJDOT having an embodied carbon clause added to NJDOT contract specifications? Will contractors be expected to submit an asphalt mix that provides EPDs to be considered for future contracts?

LCA PAVE Tool assists with analysis and quantification of the environmental impacts of existing products or processes. Retrieved from USDOT FHWA

Yes, definitely, we can dream, but it will take time. We need to identify and set the product category rule. More research is needed, maybe there will be future training opportunities on this topic from FHWA.

Q. Where is the biggest research gap when it comes to the incorporation and use of EPDs? Is it research on the pavement itself, or life cycle analysis, or something else?

EPD is not a single term, but a combination of a lot of things. In the process of determining an EPD for one pavement treatment, you must consider the process of installation, the type of pavement or asphalt mix, the binder and aggregate within the mix, etc. Because each of these processes require their own considerations, we must make the decision on what process and pavement, or asphalt mix should be evaluated first. We can then use our results to determine where the use of EPDs would be most helpful, or which process should be studied next. In other words, we cannot do everything at once, but rather start very specifically and focused, and then move out.

The five steps of developing Environmental Product Declarations (EPD). Retrieved from Tech Brief: Building Blocks of Life Cycle Thinking

 Q. Has NJDOT had an opportunity to use or test the FHWA LCA Pave Tool? If so, how does it use the tool?

I have used that tool before, but I just use it as a general gauge as I don’t have any real-time data currently. I will need training in the future on how to efficiently use the tool based on actual data. I also think this tool will be helpful in the future for determining if our results are realistic. Our research team members are using this tool.

 Q. How are you feeling about this initiative?

As a state government employee, I see this initiative as an effort that will help NJDOT be aligned with NJ’s clean energy policies. EPDs are a new topic for us, and everyone is very interested in learning more about it, including me. The funding opportunity that FHWA provided allows DOTs throughout the U.S. to explore this new topic and determine its applicability in the future of pavement and asphalt design.


Resources

FHWA Climate Challenge – Quantifying Emissions of Sustainable Pavements. FHWA webpage. Retrieved at https://www.fhwa.dot.gov/infrastructure/climatechallenge/projects/index.cfm

LCA Pave Tool. FHWA webpage. Retrieved at https://www.fhwa.dot.gov/pavement/lcatool/

Emerald Eco-Label. Webpage. Retrieved at https://asphaltepd.org/published?state=NJ

What is Environmental Product Declaration (EPD) for Sustainable Project Delivery? Webpage. Retrieved at: https://www.njdottechtransfer.net/epds-for-sustainable-project-delivery/

Life Cycle Assessment: Part I Fundamentals. Webinar, FHWA Sustainable Pavements Webinar Series. Retrieved at: https://youtu.be/uaJ8wGMAPD0?si=oBHnBSN2K1589JEa

An Introduction to Life Cycle Assessment: Part II – EPDs and PCRs, FHWA Sustainable Pavements Webinar Series. Retrieved at: https://www.youtube.com/watch?v=Y4OqVR6U2Us

Sustainable Pavements Program. FHWA Webpage. Retrieved at https://www.fhwa.dot.gov/pavement/sustainability/

Sustainability Analysis: Environmental. Life Cycle Assessment (LCA). FHWA Webpage. Retrieved at: https://www.fhwa.dot.gov/pavement/sustainability/environmental/

Meijer, J., Harvey, J., Butt, A., Kim, C., Ram, P., Smith, K., & Saboori, A. (2021). LCA Pave: A Tool to Assess Environmental Impacts of Pavement Material and Design Decisions-Underlying Methodology and Assumptions (No. FHWA-HIF-22-033). United States. Federal Highway Administration. Retrieved at: https://www.fhwa.dot.gov/pavement/lcatool/LCA_Pave_Tool_Methodology.pdf

Milleer, Lianna; Ciaviola, Benjamiin and Mukherjee, Amlan. (February 2024). EPD Benchmark for Asphalt Mixtures, SIP-108. Prepared for National Asphalt Pavement Association by WAP Sustainability. Retrieved at: https://www.asphaltpavement.org/uploads/documents/EPD_Program/NAPA-SIP108-EPDBenchmarkForAsphaltMixtures-Feb2024.pdf

Ultra High-Performance Concrete (UHPC) Applications in New Jersey – An Update

UHPC for Bridge Preservation and Repair is a model innovation that was featured in FHWA’s Every Day Counts Program (EDC-6).  UHPC is recognized as an innovative new material that can be used to extend the life of bridges. Its enhanced strength reduces the need for repairs, adding to the service life of a facility.   

This Q&A article has been prepared following an interview with Jess Mendenhall and Samer Rabie of NJDOT, who provided an update on the pilot projects of UHPC around the state. The interview has been edited for clarity. 

Q.  While EDC-6 was underway, we spoke with your unit about the pilot projects being undertaken with UHPC.  Some initial lessons were shared subsequently in a featured presentation given to the NJ STIC.  Can you update us on results of those projects, and did they yield any benefits in the fields of safety or environmental considerations?

For the NJDOT Pilot Project, the thickness of the overlay was limited by the required depth for effectiveness, as well as the cost of the UHPC material and environmental permitting. To mitigate environmental permitting, we avoided any modifications to the existing elevations and geometry of the structure. Essentially, any removal of asphalt and concrete needed to be replaced to its original elevations.

UHPC overlays can significantly extend the service of bridge decks and even increase a structure’s capacity. Although safety improvements were not the primary objective of this application, there were rideability and surface drainage considerations in the design to enhance the conditions for the road users.

The environmental impacts of structural designs must be compared on the cradle-to-grave use cycle of the design at a project scale.  Having a focus on sustainability is imperative; however, it is more meaningful when resiliency is also considered.  While the greenhouse gas emissions of a volume of UHPC are higher than those of the same volume of concrete, UHPC enables the reduction in the amount of material required in structural designs and improves the durability of structures. Its exceptional compressive strength and toughness allow for the reduction of material usage. By minimizing maintenance requirements and extending the lifespan of infrastructure, UHPC reduces the consumption of materials, energy, and resources over time.

For example, we installed this overlay on 4 bridges as a preservation technique. Had we done nothing, they would have lasted approximately 10 more years. During that time they would have needed routine deck patching resulting in further contamination of the decks and in a condition that is no longer preservable and requires total deck replacement, with large volumes of concrete and much more environmental impact.

UHPC allowed us to take these decks that are still in decent shape and preserve them now with a relatively thin layer to make them exceed the service life of the superstructure and substructure.

Q. Has UHPC been incorporated into the design manual?

Figure 1. UHPC being placed by workers

It is not in our current design manual, but we are working on the revised design manual. UHPC is presently being used for all closure pores between prefabricated components, overlays, and link-slabs. I don’t think we are ready to standardize it quite yet. We used it on the 4 bridges and it will continue to be used, but we will not standardize it until the industry is more predictable and we get more experience to develop thorough guidelines and specifications. It is incorporated into projects as a special provision with non-standard items.

Q. Have you been receiving more requests to use this technology from around the state?

It is much more commonly specified by designers or requested for use on many of our projects. We have responded to nationwide inquiries from state transportation agencies and universities seeking our specifications or input on specific testing and procedures.

Q. What efforts do you think can be taken to encourage more adoption amongst local agencies, counties, etc.?

We are keen on inviting the counties to any training or workshop that we are hosting as well as sharing our lessons learned thus far.  I think they are aware of it.

Q. What kind of hurdles do you think exist that may limit widespread adoption?

It is possible that initial cost and industry experience with the material are still major limiting factors in adoption. We have also learned from specialty UHPC contractors that the innovation and availability of construction equipment geared for UHPC implementation are also lacking.  Bringing into focus the life cycle costs and with more implementations, we think many of these hurdles will be overcome. Additionally, once UHPC is used more in routine maintenance the implementation would be more frequent and widespread; we know there is interest specifically in UHPC shotcrete once it is available.

Q. Are you familiar with any training, workshops, or conferences that have been done for staff or their partners on this topic?

We participated in the Accelerated Bridge Construction (ABC) conference in Miami, Florida, the International Bridge Conference (IBC) in Pittsburgh, Pennsylvania and the New York State DOT Peer Exchange. In Delaware, we presented at the Third International Interactive Symposium on UHPC. We also participated in the development of a UHPC course for the AASHTO Technical Training Solutions (TTS formerly TC3) which is now published on the AASHTO TTS portal and available on our LMS internally. 

Q. Do you think there is any special training needed for the construction workforce to start using this technology?

Absolutely, the AASHTO TTS course and the EDC-6 workshops are geared towards the design and construction, TTS is more focused in the Construction. It’s an introduction to what to expect and how to implement it. UHPC is often used for repair projects, and many contractors may not have the experience or comfort with using the material.

Figure 2. UHPC Testing at Rutgers’ CAIT

Q. What are the results of the pilot projects of UHPC?

This Pilot projects program demonstrated that UHPC overlays can be successfully placed on various structures, the work can be completed rapidly to minimize traffic impacts — we estimated roughly four weeks of traffic disruption per stage, and the benefits of UHPC can help preserve the existing infrastructure. Compared to deck replacement, UHPC overlays can rehabilitate a bridge deck at exceptional speeds with unique constructability and traffic patterns, as implemented in all four structures. However, limitations exist, and further research is necessary to investigate the issues identified in the pilot project, but the potential of this material outweighs the existing limitations.

Q. Has there been long-term testing data developed to gather performance data?

To assess the performance of the UHPC overlay, we put together a testing program to include NDT as well as physical sampling and lab testing. This objective will be accomplished by first establishing baseline conditions through an initial survey followed by periodic monitoring of the UHPC-overlaid bridges over succeeding years. This will help NJDOT assess the performance of UHPC as an overlay. Overall, the results show the overlay bond is performing well.

Q. Has the data from the pilot project been used to research further applications?

Further applications for UHPC overlay are on new bridge decks/superstructures, and the data from UHPC overlay research project are being used for these projects. There is an interest in header reconstruction with UHPC. If deck joints need to be replaced, they should be constructed with conventional HPC with UHPC at the surface to provide the same overlay protection over the entire structure. Also, self-consolidating and self-leveling UHPC was preferred for the full-depth UHPC header placement to ensure proper consolidation around tight corners and reinforcement. This will be further explored for maintenance operations as well.

For future projects, in lieu of full-depth header reconstruction in a single lift, a partial depth header removal and reconstruction or alternatively two lifts of header concrete should be evaluated to coincide with the deck overlay, in which case the benefits of the fast cure times from UHPC can still be realized. Two of the four bridges experienced air voids throughout the placement. A UHPC slurry with no

fibers was placed in the identified air voids; since the voids contained exposed fibers, they were considered to create adequate bonding with the UHPC slurry.

Resources

NJDOT Technology Transfer (2021, November). Stronger, More Resilient Bridges: Ultra High-Performance Concrete (UHPC) Applications in New Jersey.  Interview with Pranav Lathia, Retrieved from:  https://www.njdottechtransfer.net/2021/11/29/uhpc-stronger-more-resilient-bridges/

Mendenhall, Jess and Rabie, Samer. (2021, October 20). UHPC Overlays for Bridge Preservation—Lessons Learned. New Jersey Department of Transportation. https://www.njdottechtransfer.net/wp-content/uploads/2021/11/NJDOT-UHPC-Overlay-Research-Project-EDC-6-Workshop.pdf

New Jersey Department of Transportation. (2021, October 20). NJDOT Workshop Report. New Jersey Department of Transportation. https://www.njdottechtransfer.net/wp-content/uploads/2021/11/NJDOT-UHPC-Workshop-Final-Report.pdf

Rabie, Samer and Jess Mendenhall (2022, December). Design, Construction, and Evaluation of UHPC Bridge Deck Overlays for NJDOT.  NJ STIC Presentation and Recording.  Retrieved from:  https://www.njdottechtransfer.net/2022/12/18/nj-stic-4th-quarter-2022-meeting/

Safety Behavior and Gender Split Differences in Micromobility: A Q&A Interview with Researcher


Q. How was your research funded?    

This work was supported by the National Science Foundation under a grant called “Making Micromobility Smarter and Safer”. The lead on this is Dr. Clint Andrews at Rutgers University and there are several other principal investigators. My study acts as a part of this multi-year research.  

Q.  Can you share a brief overview of your findings? Are the results surprising or unique compared to past research?    

We are one of the only studies comparing the safety behavior of cyclists and e-scooter users across genders. Without considering gender, we found that one-third of cyclists wore a helmet. We also found in our observations that e-scooter users did not wear a helmet. It speaks to how important it is to have safe micromobility infrastructure, especially knowing that people are unlikely to wear a helmet. In the U.S., even if you give everyone a helmet, they’re probably not going to wear it. That’s just how it is. Keeping people safe in other ways is paramount.  

We also found that a greater proportion of women were using e-scooters than bicycles. This is important because cycling has long been a male-dominated mode of transportation, for a variety of reasons. That is true across the world. There are studies that suggest women are less likely to cycle to work because of clothing like wearing a skirt or dress or heels, or fears of sweating. E-scooters remove that hurdle since they are not as prohibitive in terms of clothing and require less physical exertion. So, the vehicle type itself may make a difference. Moreover, women place more importance on bike lane infrastructure than men.  If we are seeing that e-scooters are the preferred mode for females, perhaps e-scooters can help narrow the gender gap in micromobility. 

Q.  Can you talk a little bit about the methods used for this study? How are these methods different from past research? Why did you choose to use traffic cameras for your observations?

This work was done using manual observations, a common method in micromobility studies. Previous research had used observations collected in the field. Instead of having observers in the field, we observed traffic camera footage at one intersection. Because we were observing gender and race as well as group behavior, the footage was useful as it allowed us to pause when needed. It was also less resource intensive than having a person stand in the field since no travel expenses were associated with the analysis.  

Q.  What challenges have you found in working with and interpreting traffic camera footage? With the improvement of AI technologies, do you think there will be an opportunity to automate this process in the future?  Are there any limitations you expect from this type of innovation?  

It is very time consuming and tedious to analyze this much camera footage. We analyzed 35 hours of footage. I would love to have analyzed more, but you have to draw the line somewhere depending on the resources available for the research or project study. Most of the time, we fast forwarded until a micromobility user was detected, but it still requires undivided attention. There is a possibility with current technology to incorporate AI technologies: to use computer vision to detect humans, which then can be manually viewed by a human to assess micromobility mode, gender, and helmet use. This would likely reduce the manual labor… It would be interesting to compare the computer vision model to the work I have done… Nonetheless, computer vision does not differentiate properly between pedestrians and e-scooter users, so it is prone to misidentification, which would lengthen the time taken to observe manually.  

At this point, computer vision cannot detect gender, helmet use, and group riding properly from traffic camera footage. More high-resolution images would be needed to differentiate gender and helmet use (like unobstructed face images) and group riding requires context clues like making eye contact, waiting for one another, etc. AI has the potential, but it is not there yet.  As time consuming as it is, I am confident that we detected every person, which is why we chose to observe the footage ourselves.  

Q.  What are the limitations of this study? Do you have plans for future research to address these?  How would you like to expand your research on this topic?   

The main limitation is the geographical scope of this research; it’s a lot of work for one city. We only analyzed the behavior of micromobility in one location, Asbury Park. It isn’t clear how much the results will translate from one location to another. Mode of transportation and behavioral use depends on many different factors that vary from location to location. There is evidence that the gender gap is smaller for e-scooter users in Brisbane, Australia, but not to the extent observed in Asbury Park. Same goes with helmet use. A larger scale study would be useful. Other limitations include the types of micromobility modes: we only observed shared e-scooters and privately owned bicycles in Asbury Park. So, we’re comparing two different vehicles and two different share types to one another. When analyzing the data, we must consider both of these factors. For example, are behaviors attributed solely to the vehicle or to the share type? Probably both. When you’re looking at the gender gap, is it because it’s an e-scooter or is it because it’s shared that there is a narrower gender gap?  

 An analysis comparing shared and privately owned e-scooters with shared and privately owned bicycles would be great. Differentiating between e-bikes and bicycles would be great too, although the resolution of traffic camera footage makes it very hard to differentiate between the two. Even with an observer onsite, it would be hard to detect, so you would need a survey, but this could alter behavior. In Asbury Park, a lot of people have privately owned e-scooters now, so we could do another study in 1.5-2 years and get additional insights in the same location.  

E-bikes are a growing mode of transportation, but even with traffic camera footage, it is very hard to tell an e-bike apart from a bicycle, so maybe in that case you would need somebody on site actually observing. You’re losing the ability to pause footage, but it might be more useful if you’re looking at e-bikes. Race and age were also very difficult to observe from the footage. It could be easier if someone was in person to observe in addition to the traffic camera footage. Even then, without asking directly the age and race/ethnicity of the user, there will be bias. There are a lot of different things to consider; it really depends on what the question is.  

Q.  How would you like this research to inform transportation agencies and practitioners in New Jersey and elsewhere?    

There are several key points. Users of shared e-scooters and privately owned bicycles are different and behave differently. E-scooter users are more likely to take risks like not wearing a helmet or riding on the road. Planners must ensure that the infrastructure keeps them safe. That is, implementing dedicated protected bike lanes that are connected to a greater network and adding traffic calming measures to slow the speeds of motor-vehicles like raised crosswalks or narrower traffic lanes.

Understanding the reasons behind lane use is important as well, as there are concerns for pedestrian safety. Our research observed that lane use was different; for example, 7 percent of male cyclists rode on the sidewalk, compared to 28 percent of female e-scooter users.

Additionally, having a shared e-scooter system in a city can increase female participation in micromobility use. It is a more gender equitable mode than bicycles. Other agencies might want to implement an e-scooter share program in their town.  

Q.  Your research shows that women were more likely than men to ride on the sidewalk while using an e-scooter or bike. Given that this strategy is illegal in most parts of the country, how can planners, engineers and policymakers use this information to increase feelings of safety for female micromobility users?     

This is really interesting. From my research, there is not a lot that I could say. Implicitly, one of the reasons for someone to ride on the sidewalk instead of the road is that they feel safer on the sidewalk. There is a need to ensure that micromobility users feel just as safe on the road–that is, implement a dedicated and protected bike lane, and provide a clear separation from motor-vehicles.

From our work, we know that there are other more complex factors at play: our research had clear results for road lane use with the implementation of the bike lane, but less clear ones for sidewalk use: sidewalk use was not significantly reduced by the presence of a pop-up bike lane. To encourage safe road use, ensuring a complete network would be a start. The pop-up bike lane was not connected to another bike lane going downtown, for instance. If you’re already coming downtown on the sidewalk, you might be more likely to stay there given the existing curb that would need to be crossed to go from the sidewalk to the pop-up bike lane.  

Q.  NJDOT is sponsoring a program to ensure the implementation of the Statewide Bicycle and Pedestrian Master Plan. In what ways could this master plan or a future one align with the findings in your study?  

The results of this study reinforce that implementing a bike lane provides a layer of safety for micromobility users. Nearly all the increase in bike lane usage came from a reduction in traffic lane usage, not in sidewalk usage. There is so much research out there that shows that bike lanes save lives; in the case of a crash, someone in a bike lane is less likely to be injured. Ensuring that plans accommodate both bicycles and e-vehicles–like e-bikes and e-scooters–is also paramount.  

Q.  The Biden Administration has set a goal to achieve a net zero emissions economy by 2050. How might a shift toward micromobility help the nation reach its climate and carbon emission goals?    

Bicycles are zero emission vehicles. E-bikes and e-scooters produce few emissions, especially privately owned ones since they don’t require rebalancing. Rebalancing shared vehicles requires a car or van and those gasoline emissions are absorbed by those shared e-scooters. Having an e-vehicle do that for rebalancing helps to reduce those emissions. Bicycle-friendly infrastructure, which reduces motor-vehicle infrastructure such as the number of traffic lanes, or parking, can also reduce motor-vehicle use and induce more environmentally friendly travel.   

Q.  How could a focus on reaching these climate goals impact the way that planners and engineers design streets?    


Resources

Blickstein, S.G., Brown, C.T., & Yang, S. (2019). “E-Scooter Programs Current State of Practice in US Cities.” Retrieved from https://njbikeped.org/e-scooter-programs-current-state-of-practice-in-us-cities-2019/

Marshall, H. (2023). “How do Female Cyclists Perceive Different Cycling Environments? – A Photo-elicitation study in Stockholm, Sweden.” Retrieved from https://gupea.ub.gu.se/handle/2077/78209

NJDOT Technology Transfer. (2020). “Tech Talk! Launching Micromobility in NJ and Beyond.” Retrieved from https://www.njdottechtransfer.net/2020/02/25/launching-micromobility-in-nj-and-beyond/

NJDOT Technology Transfer. (2021). “How Automated Video Analytics Can Make NJ’s Transportation Network Safer and More Efficient.” Retrieved from https://www.njdottechtransfer.net/2021/11/08/automated-video-analytics/

NJDOT Technology Transfer.(2022). “Research Spotlight: Exploring the Use of Artificial Intelligence to Improve Railroad Safety”. Retrieved from https://www.njdottechtransfer.net/2022/08/19/researchspotlightairailroadsafety

Rupi, F., Freo, M., Poliziani, C., & Schweizer, J. (2023). “Analysis of Gender-Specific Bicycle Route Choices Using Revealed Preference Surveys Based on GPS Traces.” Retrieved from https://www.sciencedirect.com/science/article/pii/S0967070X2300001X

Salazar-Miranda, A., Zhang, F., Maoran, S., & Ratti, C. (2023). “Smart Curbs: Measuring Street Activities in Real-Time Using Computer Vision,” Retrieved from https://www.sciencedirect.com/science/article/pii/S0169204623000348?casa_token=XPecGlOM6UQAAAAA:vnISsmV2aoJ3iVJefEeqjM24R5izcs66bvukCQObjuSWGTNokotT4CG_1h8UfLih16wn3FMg_Jo [DA1] [KR2] 

Von Hagen, L.A., Meehan, S., Younes, H., et. al. (2022), “Asbury Park Bike Lane Demonstration,” Retrieved from https://storymaps.arcgis.com/stories/c014811ac0c14735bc9c9adc2639e88f.

Younes, H., Noland, R., & Andrews, C. (2023). “Gender Split and Safety Behavior of Cyclists and E-Scooter Users in Asbury Park, NJ,” Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S2213624X2300127X#b0055.

Younes, H., Noland, R., & and Von Hagen, L.A. (2023). “Are E-Scooter Users More Seriously Injured than E-Bike Users and Bicyclists?” Retrieved from https://policylab.rutgers.edu/are-e-scooter-users-more-seriously-injured-than-e-bike-users-and-bicyclists/.


Research Underway to Address Travel Needs of Cognitively Divergent Individuals in Complete Streets Plans

The Complete Streets planning approach pushes for a future in which people of all ages and abilities can safely travel. Recently signed NJ legislation takes an important step toward this vision by ensuring that the travel needs of cognitively divergent individuals are addressed in Complete Streets Plans.

In January 2023, Governor Phil Murphy signed S-147 into law, directing the New Jersey Department of Transportation (NJDOT) to update its Complete Streets policy to consider and implement design elements and infrastructure projects that promote the ability of persons diagnosed with autism spectrum disorder (ASD) and persons with intellectual and developmental disabilities (IDDs) to travel independently.

This requirement follows important research conducted by Rutgers CAIT and VTC and funded by NJDOT, in which the travel behavior of over 700 adults with autism spectrum disorder (ASD) was studied. The research concluded that individuals with ASD, seeking to travel independently, experience extraordinary transportation barriers that are complicated by the state’s auto-oriented street design and land uses. With fewer such persons driving cars, an improved network of walking and biking infrastructure opens a world of opportunities for engagement in civic life and to reaching essential destinations via public transportation.

The Complete Streets Summit event will include a session on efforts underway to revise policies to promote travel independence for ASD with IDD persons.

NJDOT has undertaken a project that seeks to address how to accommodate the travel needs of people with ASD and/or IDDs through policy and design. The Department’s Bureau of Safety, Bicycle and Pedestrian Programs has engaged the Rutgers-Voorhees Transportation Center, NV5, Toole Design Group and a working group of NJDOT planners and engineers to assist with addressing the travel needs of cognitively divergent persons – and with meeting the requirements of the legislation.

The research team is developing a primer on Complete Streets and neurodivergence and will use the information gathered to help NJDOT develop universal design guidelines that will ensure the Department’s Complete Streets policy considers the needs of those with ASD and IDDs. The team will be sharing more information at the upcoming 2023 New Jersey Complete Streets Summit on November 1st. Not yet registered? Register Here.

More information on the past and ongoing research underway and how cognitive functioning can differ among members of ASD and IDD populations is summarized in this short article, Complete Streets for Individuals with Autism Spectrum Disorder (ASD) and Intellectual and Developmental Disabilities (IDDs), on the NJ Bicycle and Pedestrian Resource Center website.

Research Spotlight: NJ Transit Grade Crossing Safety

A recently completed research study on NJ TRANSIT grade crossing safety focuses on identifying locations for rail grade crossing elimination. Researchers from Rutgers’ Center for Advanced Infrastructure and Transportation (CAIT), Asim Zaman, P.E., Xiang Liu, Ph.D., and Mohamed Jalayer, Ph.D., from Rowan University, developed a methodology using 20 criteria to narrow a list of 100 grade crossings to ensure appropriate identification for closure. The process helps NJ TRANSIT and New Jersey Department of Transportation (NJDOT) to direct limited funds to areas of greatest need to benefit the public.

Across the country, 34 percent of railroad incidents over the past ten years have occurred at grade crossings. The elimination of grade crossings can improve public safety, decrease financial burdens, and improve rail service to the public.

According to the proposed methodology, the 20 crossings recommended for closure located in Monmouth County (60%), Bergen County (25%), and Essex County (25%).

According to the proposed methodology, the 20 crossings recommended for closure located in Monmouth County (60%), Bergen County (25%), and Essex County (25%).

The researchers ranked grade crossings in New Jersey using the following data fields: crash history, average annual daily traffic, roadway speed, roadway lanes, length of the crossing’s street, weekday train traffic, train speed category, number of tracks, access to train platforms, intersection angle, distance to alternate crossings, distance to emergency and municipal buildings, whether emergency and municipal buildings are on the same street, and date of last or future planned signal and surface upgrades. This process resulted in a final list of 20 grade crossings eligible for elimination.

To understand how this study will be used, we conducted an interview with NJTRANSIT personnel Susan O’Donnell, Director, Business Analysis & Research, Ed Joscelyn, Chief Engineer – Signals, and Joseph Haddad, Chief Engineer, Right of Way & Support.

Q. How will the report inform decision-making? 

It is important to have solid research and strong evaluation criteria, such as developed by this study, on which to base decisions for grade crossing elimination. In addition to the study, we looked at what other state agencies and transit agencies have done with grade crossing elimination, as well as criteria recommendations from Federal Highway Administration (FHWA) and Federal Railroad Administration (FRA). Following up on this study, NJ TRANSIT and NJDOT are considering next steps that would be needed to close the 20 identified grade crossings. In New Jersey, the Commissioner of Transportation has plenary power over the closing of grade crossings.

Q. What other information will be needed to assess these locations? 

Local concerns about grade crossing elimination tend to focus on traffic re-routing, including the possible impacts on neighborhoods, time needed to reach destinations, and emergency vehicle access to all parts of a community. The criteria established by the study addressed these areas of concern. Prior studies have determined that the road networks around the identified locations are adequate to accommodate re-routed traffic. The current research study took into account the findings from those prior studies. As each project moves forward, NJDOT will determine if additional information will be needed.

Q. Is elimination of any of these grade crossings part of NJ TRANSIT’s capital program? 

All of the closings are part of the capital program. Funding for the grade crossing elimination comes from the federal government and NJ TRANSIT. NJ TRANSIT funding is in place to close the crossings.

Q. Are there benefits of the research study beyond identification of the 20 grade crossings?

The research study developed the criteria and process for identifying grade crossings for elimination. This framework can be used in the future to assess other grade crossings for possible elimination. NJ TRANSIT is grateful to NJDOT for funding this important research project to improve safety.

For more information on this research study, please see the resources section below.


Resources

Zamin, A., Alfaris, R., Li, W., Liu, Z. Jalayer, M., Hubbs, G., Hosseini, P., Calin, J.P., Patel., S. (2022). NJ Transit Grade Crossing Safety. [Final Report].  New Jersey Department of Transportation, Bureau of Research.  Retrieved from https://www.njdottechtransfer.net/wp-content/uploads/2023/02/FHWA-NJ-2022-005.pdf

Liu, Z., Jalayer, M., and Zamin, A. (2022). NJ Transit Grade Crossing Safety. [Technical Brief]. New Jersey Department of Transportation, Bureau of Research.  Retrieved from https://www.njdottechtransfer.net/wp-content/uploads/2023/02/FHWA-NJ-2022-005-TB.pdf

Research Spotlight: Calibration and Development of Safety Performance Functions for New Jersey

In 2019, a team of researchers from New York University and Rutgers University examined ways to calibrate and develop Safety Performance Functions (SPFs) to be utilized specifically to address conditions on New Jersey roadways. SPFs are crash prediction models or mathematical functions informed by data on road design. These data include, but are not limited to, lane and shoulder widths, the radius of the curves, and the presence of traffic control devices and turn lanes. With these data, SPFs help those tasked with road design and improvement to build roads and implement upgrades that maximize safety.

The Highway Safety Manual (HSM) presents SPFs developed using historic crash data collected from several states over several years at sites of the same facility type. These SPFs data cannot be transferred to other locations because of expected differences in environment and geographic characteristics, crash reporting policies and even local road regulations. To help SPFs better reflect local conditions and observed data, one of two strategies is usually undertaken to fine-tune SPFs:  calibrating the SPFs provided in the HSM so as to fully leverage these data or developing location-specific SPFs regardless of the predictive modeling framework included in the HSM.

The research team, led by Dr. Kaan Ozbay (of NYU’s Tandon School of Engineering), chose to pursue both of these strategies. The research report, Calibration/Development of Safety Performance Functions for New Jersey, can be found here. A webinar highlighting the research and findings can be found here.  A monograph, supported by the NJDOT funded study and partially by C2SMART, a Tier 1 UTC led by NYU and funded by the USDOT, was also recently published and can be found here.

C2SMART Webinar highlighted the research methods, findings, challenges and technology transfer efforts of the NYU-Rutgers team for this NJDOT funded research project.

SPFs can be utilized at several levels. At the network level, researchers and engineers use SPFs to identify locations with promise for improvement. SPFs can be used to predict how safety treatments will affect the likelihood of crashes based on traffic volume and facility type. SPFs can be used to influence project level design by showing the average predicted crash frequency for an existing road design, for alternate designs, and for brand-new roads.

SPFs also can be used to evaluate different engineering treatments. In this case, engineers and researchers return to a site where a safety countermeasure has been installed to collect and analyze data to see how the change has affected crash frequency. They examine before and after conditions and measure if the prediction made using the SPF was accurate or needs improvement (Srinivasan & Bauer, 2013). In the end, SPFs are only as good as the data used in their development.

NJDOT and the NYU-Rutgers team set out to calibrate SPFs using New Jersey’s roadway features, traffic volumes and crash data, and if necessary, to create new SPFs that reflect conditions in the state. The facility types considered for this research project included segments and intersections of rural two-lane two-way, rural multilane, and urban and suburban roads. In examining these datasets, the researchers identified areas where data processing improvements could be made to enhance the quality or efficiency in use of the data in addition to pursuing the stated goal of developing New Jersey-specific SPFs.

For example, utilizing the data provided by NJDOT, the research team developed methods for processing a Roadway Features Database of different kinds of road facilities. The researchers utilized the Straight Line Diagrams (SLD) database, which offers extensive information about the tens of thousands of miles of roadways in New Jersey, but observed issues and errors in the SLD database that required corrections. For example, the research team utilized Google Maps and Google Street View to conduct a manual data extraction process to verify information in the SLD database (e.g., confirm whether an intersection was an overpass, number of lanes, directionality) and extract missing variables, such as the number of left and right turn lanes at intersections, lighting conditions, and signalization needed for the analysis.

The research team using Google Street View to identify missing data points.

The research team also needed to develop programming code to correctly identify the type and location of intersections and effectively work with available data. The team developed a novel “clustering-based approach” to address the absence of horizontal curvature data using GIS centerline maps.

Utilizing Google Maps (Left) and the state’s Straight Line Database (Right), researchers were able to identify missing paths in the database that contributed to inconsistent data.

Police reports of crashes often have missing geographic identifiers which complicates analytical work such as whether crashes were intersection-related. In NJ, police are equipped with GPS devices to record crash coordinates but this crash information is somewhat low in the raw crash databases before post-processing by NJDOT. The researchers employed corrective methods and drew upon other NJ GIS maps to provide missing locations (e.g., Standard Route Identification or milepost).

The processing challenges for roadway features, traffic volumes and crashes encountered by the research team suggest the types of steps that can be taken to standardize and streamline data collection and processing to secure better inputs for future SPF updates. Novel data extraction methods will be needed to minimize labor time and improve accuracy of data; accurate crash data is integral to employing these methods.

The research team modified the spreadsheets developed by the HSM and used by the NJDOT staff. The calculated calibration factors and the developed SPFs are embedded in these spreadsheets. The users can now select whether to use the HSM SPFs with the calculated calibration factors or the New Jersey-specific SPF in their analyses

The researchers’ data processing and calibration efforts sought to ensure that the predictive models reflect New Jersey road conditions that are not directly reflected in the Highway Safety Manual. The adoption of this data-driven approach can make it possible to capture information about localized conditions but significant expertise is required to carry out calibration and development analyses. With more research—and improved data collection processes over time —the calibration and development of SPFs holds promise for helping New Jersey improve road safety.


Resources

Bartin, B., Ozbay, K., & Xu, C. (2022). Safety Performance Functions for Two-Lane Urban Arterial Segments. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4175945

C2SMART. (2020, September 23). Webinar: Bekir Bartin, Calibration and Development of Safety Performance Functions for New Jersey . Retrieved from YouTube: https://youtu.be/IRalyvjDaFM

Ozbay, K., Nassif, H., Bartin, B., Xu, C., & Bhattacharyya, A. (2019). Calibration/Development of Safety Performance Functions for New Jersey [Final Report]. New Jersey Department of Transportation Bureau of Research. Retrieved from https://www.njdottechtransfer.net/wp-content/uploads/2020/07/FHWA-NJ-2019-007.pdf

Ozbay, K., Nassif, H., Bartin, B., Xu, C., & Bhattacharyya, A. (2019). Calibration/Development of Safety Performance Functions for New Jersey [Tech Brief]. Rutgers University. Department of Civil & Environmental Engineering; New York University. Tandon School of Engineering. Retrieved from https://www.njdottechtransfer.net/wp-content/uploads/2020/07/FHWA-NJ-2019-007-TB.pdf

Srinivasan, R., & Bauer, K. M. (2013). Safety Performance Function Development Guide: Developing Jurisdiction-Specific SPFs. The University of North Carolina, Highway Safety Research Center. Retrieved from https://rosap.ntl.bts.gov/view/dot/49505

Zone for AI to look for trespassing at railroad crossing

Research Spotlight: Exploring the Use of Artificial Intelligence to Improve Railroad Safety

Partnering with the Federal Railroad Administration, New Jersey Transit and New Jersey Department of Transportation (NJDOT), a research team at Rutgers University is using artificial intelligence (AI) techniques to analyze rail crossing safety issues. Utilizing closed-circuit television (CCTV) cameras installed at rail crossings, a team of Rutgers researchers, Asim Zaman, Xiang Liu, Zhipeng Zhang, and Jinxuan Xu, have developed and refined an AI-aided framework for detection of railroad trespassing events to identify the behavior of trespassers and capture video of infractions.  The system uses an object detection algorithm to efficiently observe and process video data into a single dataset.

Rail trespassing is a significant safety concern resulting in injuries and deaths throughout the country, with the number of such incidents increasing over the past decade. Following passage of the 2015 Fixing America’s Surface Transportation (FAST) Act that mandated the installation of cameras along passenger rail lines, transportation agencies have installed CCTV cameras at rail crossings across the country.  Historically, only through recorded injuries and fatalities were railroads and transportation agencies able to identify crossings with trespassing issues. This analysis did not integrate information on near misses or live conditions at the crossing. Cameras could record this data, but reviewing the video would be a laborious task that required a significant resource commitment and could lead to missed trespassing events due to observer fatigue.

Zaman, Liu, Zhang, and Xu saw this problem as an opportunity to put AI techniques to work and make effective use of the available video and automate the observational process in a more systematic way. After utilizing AI for basic video analysis in a prior study, the researchers theorized that they could train an AI and deep learning to analyze the videos from these crossings and identify all trespassing events.

Working with NJDOT and NJ TRANSIT, they gained access to video footage from a crossing in Ramsey, NJ.  Using a deep learning-based detection method named You Only Look Once or YOLO, their AI-framework detected trespassings, differentiated the types of violators, and generated clips to review. The tool identified a trespass only when the signal lights and crossing gates were active and tracked objects that changed from image to image in the defined space of the right-of-way. Figure 1 depicts the key steps in the process for application of AI in the analysis of live video stream or archived surveillance video.

Figure 1. General YOLO-Based Framework for Railroad Trespass Detection illustrates a step-by-step process involving AI algorithm configurations, YOLO-aided detection, and how trespassing detection incidents are saved and recorded to a database for more intensive analysis and characterization (e.g., trespasser type, day, time, weather, etc.)

The researchers applied AI review to 1,632 hours of video and 68 days of monitoring. They discovered 3,004 instances of trespassing, an average of 44 per day and nearly twice an hour. The researchers were able to demonstrate how the captured incidents could be used to formulate a demographic profile of trespassers (Figure 2) and better examine the environmental context leading to trespassing events to inform the selection and design of safety countermeasures (Figure 3).

Figure 2: Similar to patterns found in studies of rail trespassing fatalities, trespassing pedestrians were more likely to be male than female. Source: Zhang et al
Figure 3: Trespassing events were characterized by the gate angle and timing before/after a train pass to isolate context of risky behavior. Source: Zhang et. al

A significant innovation from this research has been the production of the video clip that shows when and how the trespass event occurred; the ability to visually review the precise moment reduces overall data storage and the time needed performing labor-intensive reviews. (Zhang, Zaman, Xu, & Liu, 2022)

With the efficient assembly and analysis of video big data through AI techniques, agencies have an opportunity, as never before, to observe the patterns of trespassing. Extending this AI research method to multiple locations holds promise for perfecting the efficiency and accuracy in application of AI techniques in various lighting, weather and other environmental conditions and, more generally, to building a deeper understanding of the environmental context contributing to trespassing behaviors.

In fact, the success of this AI-aided Railroad Trespassing Tool has led to new opportunities to demonstrate its use. The researchers have already expanded their research to more crossings in New Jersey and into North Carolina and Virginia. (Bruno, 2022) The Federal Railroad Administration has also awarded the research team a $582,859 Consolidated Rail Infrastructure and Safety Improvements Grant to support the technology’s deployment at five at-grade crossings in New Jersey, Connecticut, Massachusetts, and Louisiana. (U.S. DOT, Federal Railroad Administration, 2021) Rutgers University and Amtrak have provided a 42 percent match of the funding.

The program’s expansion in more places may lead to further improvements in the precision and quality of the AI detection data and methods.  The researchers speculate that this technology could integrate with Positive Train Control (PTC) systems and highway Intelligent Transportation Systems (ITS). (Zhang, Zaman, Xu, & Liu, 2022) This merging of technologies could revolutionize railroad safety. To read more about this study and methodology, see this April 2022 Accident Analysis & Prevention article.

References

Bruno, G. (2022, June 22). Rutgers Researchers Create Artificial Intelligence-Aided Railroad Trespassing Detection Tool. Retrieved from https://www.rutgers.edu/news/rutgers-researchers-create-artificial-intelligence-aided-railroad-trespassing-detection-tool

NJDOT Technology Transfer. (2021, November 8). How Automated Video Analytics Can Make NJ’s Transportation Network Safer and More Efficient. Retrieved from https://www.njdottechtransfer.net/2021/11/08/automated-video-analytics/

Tran, A. (n.d.). Artificial Intelligence-Aided Railroad Trespassing Data Analytics: Artificial Intelligence-Aided Railroad Trespassing Data Analytics:.

United States Department of Transportation: Federal Railroad Administration. (2021). Consolidated Rail Infrastructure and Safety Improvements (CRISI) Program: FY2021 Selections. Retrieved from https://railroads.dot.gov/elibrary/consolidated-rail-infrastructure-and-safety-improvements-crisi-program-fy2021-selections

Zaman, A., Ren, B., & Liu, X. (2019). Artificial Intelligence-Aided Automated Detection of Railroad Trespassing. Journal of the Transportation Research Board, 25-37.

Zhang, Z., Zaman, A., Xu, J., & Liu, X. (2022). Artificial intelligence-aided railroad trespassing detection and data analytics: Methodology and a case study. Accident Analysis & Prevention.