The New Jersey Micromobility Guide serves as a resource for micromobility users across the state, collecting and summarizing the laws and safety best practices that can make riders safer. Micromobility, which includes e-bikes, e-scooters, and other low-speed devices, is an affordable, energy-efficient, eco-friendly alternative to driving. For short-distance travel, micromobility can replace car trips, thus lowering transportation costs and helping to reduce congestion and car parking demand on local streets. For longer trips, this guide clarifies if and how micromobility riders can bring their devices onto public transportation. By providing tips, answering common questions, and clarifying how different devices are regulated, this guide serves as a resource that promotes the safe and legal use of e-bikes, e-scooters, and other forms of micromobility throughout New Jersey.
NJDOT Bicycle and Pedestrian Resource Center. (2025). New Jersey Micromobility Guide. Retrieved from https://njbikeped.org/new-jersey-micromobility-guide-2025
Dr. Hannah Younes is a Senior Research Specialist at the Voorhees Transportation Center in the Edward J. Bloustein School of Planning and Public Policy, Rutgers University. Her research interests revolve around sustainable and safe transportation. In her role at Rutgers University, Dr. Younes focuses on crash and medical records research, built environment and geometric roadway design, and active travel research. She has qualitative methodological experience with focus groups, surveys, and interviews and quantitative methodological expertise with statistical regression methods. Dr. Younes has extensive experience interviewing transportation practitioners in numerous fields, including state departments of transportation, transit agencies, municipal engineers, and academic researchers. Before coming to Rutgers, she was a research assistant for the Maryland Transportation Institute (MTI) in the Department of Civil and Environmental Engineering at UMD, focusing on transport geography issues.
Samuel Rosenthal, AICP, is a Research Project Coordinator at the Alan M. Voorhees Transportation Center at Rutgers University. As a staff member at the New Jersey Bicycle and Pedestrian Resource Center (NJ BPRC), Sam contributes to planning for safe, equitable, and accessible active transportation in New Jersey. Sam conducts literature reviews and research to identify and synthesize best practices for a range of transportation planning topics. He has experience with public engagement including conducting walk and bike audits, facilitating focus groups, and creating, programming, and analyzing surveys for projects related to active transportation and transit. Sam’s recent projects have focused on developing resources related to e-bike and e-scooter legislation and safety, as well as research on Complete Streets approaches that improve safety for those with autism spectrum disorder and intellectual and developmental disabilities.
This study examines the structural health and socioeconomic variables influencing the condition of all bridges in New Jersey. Starting by examining the resilience of bridges under various load scenarios (Average Daily Traffic (ADT)/Live Loads, Environmental Loads/Conditions) categorized by bridge materials.
First, a Kaplan Meier model was used to determine the time to failure for the various load scenarios categorized by the bridge materials; giving the percentage of failed bridges at year 0,50,100,150 and 200 indicating the proportion of bridges that remain functional without failure after the marked years; additionally, a Cox PH model was used to determine the relationship between the covariates and the bridge failure risks.
Second, we investigate the impacts of local socioeconomic factors on bridge conditions. Since tolls, gas taxes, user fees and taxes, and federal road funds, provide most of the funding for bridge reconstruction, this analysis sought to identify any relationships between (a) bridge condition history (the time it takes to reach failure criteria), (b) the percentage of good and fair bridges in each county, and (c) the socioeconomic factors that are present. Using K-means clustering we first conducted an exploratory analysis to evaluate the data behavior, then using Multiple Linear Regression we found how much the various factors combination impacted the bridge health. The importance of each affecting factor was ranked through Random Forest.
The results showed that AADT contributed 31.4% to the bridge deterioration, followed by Population contributing 22.73%, and Business 16.38%, with Median Income contributing to the 1.08%. This reinforces the need for targeted funding relying on each locality’s specific needs to support equitable bridge maintenance and improve overall infrastructure quality. This insight, combined with time-to-failure analysis, suggests prioritizing specific bridge types in low-income areas to ensure longevity despite limited funds.
Lawrencia Akuffo is a civil engineer and researcher whose work bridges the fields of structural engineering, data science, and infrastructure resilience. As a Graduate Fellow at Rowan University’s Department of Civil and Environmental Engineering, her research focuses on structural health monitoring and remaining capacity estimation of bridges and prestressed concrete girders using advanced modeling tools like Abaqus and machine learning. Lawrencia’s innovative studies integrate LiDAR data, point cloud analysis, and socioeconomic modeling to understand infrastructure performance and deterioration across New Jersey’s bridges. Her multidisciplinary approach, which combines statistical modeling, structural simulation, and data-driven insights, positions her at the forefront of next-generation transportation infrastructure research.
Protective barriers in transportation engineering require materials that are lightweight, highly energy-absorbing, and capable of mitigating vibration. Auxetic metamaterials, with their negative Poisson’s ratio (NPR) arising from geometric design, offer significant potential. However, conventional lattices often suffer from low load-bearing capacity and unstable deformation, restricting their application in infrastructure.
This study designs a multi-stage auxetic metamaterial that integrates rotating, re-entrant, and chiral mechanisms to enhance energy absorption, load-bearing capacity, and structural stability, evaluated through quasi-static compression analysis. Results show that the lattice sustains NPR behavior up to 60% strain, forms two distinct stress plateaus, and achieves nearly twice the specific energy absorption of existing multi-stage designs while maintaining stable deformation.
By combining lightweight architecture, enhanced energy absorption, and reliable multi-phase stability, the proposed metamaterial provides a promising solution for crash barriers, bridge protection, and vibration-damping systems in transportation engineering.
Linzhi Li is a Ph.D. student in the Department of Civil, Environmental, and Ocean Engineering at Stevens Institute of Technology, supervised by Professor Yi Bao. Her research focuses on the design and optimization of mechanical and auxetic metamaterials for vibration mitigation, energy absorption, and structural protection in civil and transportation engineering. Her recent work develops multi-stage auxetic lattices that integrate rotating, re-entrant, and chiral mechanisms to achieve enhanced stability and load-bearing performance.
Heavy traffic volumes, frequent lane merges, toll plazas, and complex interchanges often create conditions for sudden and forceful vehicular stops, known as harsh braking (HB). Traditional safety studies rely on historical crash records, a reactive approach that delays countermeasures. Since HB events are continuously captured by connected-vehicle telematics, their spatial and temporal patterns offer a proactive surrogate for identifying crash-prone roadway segments. Therefore, this study evaluates the potential of harsh braking (HB) events as a surrogate measure of crash risk on New Jersey interstate highways. More than 8.5 million Drivewyze telemetry records and 45,000 police-reported crashes from July to December 2024 were analyzed. HB events were identified by a deceleration threshold of 6 ft/sec² (approximately 0.2g) and spatially matched to one-mile highway segments along with crash data. Descriptive analysis revealed strong spatial clustering of HB events and crashes along high traffic volume corridors such as I-95, I-80, I-78, and I-287, particularly near toll plazas and complex interchanges. Seasonal patterns showed HB counts peaking in late fall, coinciding with higher traffic congestion and adverse weather conditions. Statistical modeling using Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regressions demonstrated a positive and significant relationship between HB events and crash counts. In the preferred ZINB model, the HB coefficient was 0.01 (p = 0.03), indicating that each additional HB event was associated with roughly a 1 % increase in expected crash frequency per segment. Although the per-event effect was modest, segments with repeated HB activity exhibited substantially elevated crash risk; for instance, an increase of 10 HB events correspond to an expected crash frequency of about 10 % higher. These findings demonstrate that crowdsourced telematics can serve as a practical, proactive tool for highway safety management, supporting early detection of high-risk locations and guiding countermeasures such as improved signage, targeted enforcement, and geometric enhancements before crash records accumulate.
Md. Tufajjal Hossain is a Ph.D. student in Transportation Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on traffic flow modeling, intelligent transportation systems, and AI-driven traffic safety analysis. His recent work includes developing real-time incident detection models using crowdsourced Waze data and designing a data-driven framework for optimal Safety Service Patrol route identification based on historical crash data. He also explores crash severity prediction using large language models to enhance roadway safety analytics. At NJIT, he serves as a Teaching Assistant and has contributed to NJDOT-funded research at the Intelligent Transportation Systems Research Center. He is the recipient of the 2025 ITSNJ Outstanding Graduate Student Award and the Best Poster Award at the 2024 ITSNJ Annual Meeting, recognizing his academic excellence and contributions to advancing intelligent and data-driven transportation systems.
Biometric sensors have long been used in cognitive psychology to measure the stress-level of individuals. These sensors can measure a variety of human behaviors that translate as stress: the movement of eyes, stress-induced sweat, and heart rate variability. Recently, this research strategy has moved beyond psychology and into disciplines like transportation planning, to provide an alternative approach to researching micromobility and stress.
We spoke with Dr. Wenwen Zhang, associate professor at the Edward J. Bloustein School of Planning and Public Policy at Rutgers University, about her experience learning about and using biometrics for a micromobility study. Dr. Zhang’s research, “Rider-Centric Approach to Micromobility Safety” examines the stress levels of micromobility users as they transverse a varied path through an urban space.
Q. How is your research funded?
A. Funding comes from multiple sources. The first source is a seed grant from the Rutgers Research Council which supports an interdisciplinary pilot project. Through this grant, we purchased biometric sensors and hired students to conduct a literature review and develop a research design. We also processed the collected pilot data and paid for participation incentives under this funding. I presented preliminary findings from this study, Rider-Centric Approach to Micromobility Safety, at the 2023 NJDOT Research Showcase. At the time that I presented it, I had 24 samples. The presentation ended up inspiring several people who attended the Research Showcase to volunteer as participants—which increased the sample size to 30.
Our other source of funding came from an external grant from the C2Smart University Transportation Center (UTC) at NYU. We used this resource to support obtaining additional stress sensors, data analysis, cleaning, preprocessing, and modeling, as well as collecting more sample data for the E-scooter and bicycle experiments.
Q. How did you get interested in using biometrics sensors (e.g., eye tracking glasses, galvanic skin sensor, heart rate monitors) to study micromobility safety? How does this research differ from your past work?
A. Before I used biometric sensors, most of my work used passive travel behavior data. For example, to determine the revealed preferences of mode and route choices and risk factors, we used travel trajectory or existing crash big data to develop statistical models. I have found that the entire process is very passive, especially since we only explore risk factors after traffic accidents. It’s surprising that in the research field today we know so little about how human beings actually navigate urban environments while using different travel modes and how it relates to perceived safety. I wanted to explore questions like what is their gaze behavior? How do they feel while they travel using different modes? How do they feel traveling on roads with different design features and how is that going to influence their travel satisfaction or experience overall?
Dr. Robert Noland, Distinguished Professor at the Rutgers Bloustein School, suggested I investigate the use of biometrics in planning studies. As I dug more into the literature, I realized that biometrics in transportation is a very fascinating topic that I wanted to get into. Once I did experiments in the field, I realized that I really enjoyed talking with different people about how they perceive the built environment while they travel. Biometrics provide richer data compared with revealed preference data that I used to work with.
Q. In your research, you noticed that some corridors were more stress-inducing (according to biometric sensors) than expected, despite properly designed safety infrastructure. How do you think this discovery may affect how planners and engineers look at urban road design and micromobility safety?
A. This study collected one-time cross-sectional data. We asked people to walk around an area and tell us whether they feel stressed or not. If they are feeling stress, even in the presence of a safety improvement, it does not necessarily mean that the implemented safety design is not working. For example, in New Brunswick, we observed that a lot of people found it stress-inducing to cross Livingston Avenue, although it has been the subject of a road diet and has several pedestrian safety features incorporated into the new design. While outside our scope of research, one way to understand the impact of the safety infrastructure would be to conduct a “before” and “after” study. This leaves an opportunity for more research, to see how effective the pedestrian-only infrastructure is in reducing stress level. Potentially, it can provide evidence to support pedestrian-only design. Biometric sensors used in a “before and after” study can help us to answer which infrastructure is more preferred.
Q. You are in the process of collecting data for cyclists and e-scooters using the same method, what are your principal objectives in addressing this segment? Do you expect the results to be different?
Dr. Zhang conducted one pilot e-scooter experiment at Asbury Park, NJ in 2022 to test out the devices and examine how to set up research experiments. She equipped the e-scooter rider, Dr. Hannah Younes, post-doc researcher at the Rutgers Bloustein School, with an eye tracking glass, a GSR sensor on the hand, and a 360-degree camera on top of the helmet.
A. Yes, absolutely, different travel modes will likely alter a person’s expectation for a safe travel environment. For example, we noticed a big difference in the enjoyment of pedestrians and e-scooters on the same path through a park. We had thought that the e-scooter users would enjoy the ride as the pedestrians had, however, the pavement was too rough for the small wheels of the e-scooters. Although the park was walking-friendly, it was not friendly for e-scooters. This shows that each of these micromobility modes needs different kinds of support to feel safe and comfortable.
Q. What are the limitations to this study? Do you have plans for future research to address this? How would you like to expand your research in this topic?
A. Each of the biometric sensors has limitations. For example, eye trackers face some difficulty when identifying the pupils of a participant in direct sunlight. As a result, the eye tracker renders a low eye tracking rate. Eye trackers also work better with darker eyes as the eye movements are more readily recognized. The eye trackers, kept on glasses, also restrict individuals who wear glasses from participating. The unfortunate result of this is that it often excludes a lot of senior people from the experiment. This issue may be alleviated as we are obtaining additional funding to obtain prescription lenses for eye trackers.
GSR sensors use low voltage on skin to measure skin conductivity, which may interfere with electric health devices. This limits individuals from participating if they have an electric health device like a pacemaker on or in their body. We purposefully excluded this population from participating to align with IRB (Institutional Review Board) protocol and to mitigate any risks.
Another limitation of the study is that we must collect sample data one by one, which is a time-consuming process. We can only collect a very small sample compared to a traditional statistical model kind of study, which may have access to thousands of records in the sample. From our literature review, biometrics sensor studies typically involve 20 to 30 participants, but for each participant we have a very rich dataset. For each participating volunteer, we end up with over one gigabyte of data. The limited number of participants may make it harder to generalize results to the entire population, and people may question the results applicability. In some ways this data is similar to the results of qualitative studies, where we have richer information but small sample size, rendering some generalizability issues.
Feelings of safety were measured using the traditional self-report survey as well as biometric trackers like Heart Rate Trackers, Eye trackers and GSR (pictured above).
Q. What challenges have you found in working with biometrics sensors, or in the interpretation of output measures?
A. Theeye tracker and heart rate measures are reliable, but some biometrics have posed challenges. The GSR (galvanic skin response sensor), which tests your sweat level, is very sensitive to humidity and time of the day. The sensor also picks up on sweat resulting from physical exertion, making it difficult to distinguish between stress-induced sweat and physical sweat.
Interpretation of output measures for this metric requires data cleaning and processing to eliminate the effect of sweating from physical exertion. We try to decompose the data to separate the emotional peak from the sweating caused by physical activity using various algorithms. We are still underway testing out different algorithms to clean up the data. So far, we have found that GSR data are very real-time in nature and a good indicator for stress level but are very noisy data and requires some manual processing. This means we spend a lot of time preprocessing the collected data before conducting data analysis.
Q. How do you expect this research to inform transportation agencies in New Jersey and elsewhere?
A. This type of research captures such rich data on travel behavior itself. Most of the literature using biometrics has been focused on driving, so this research expands the perspective. Here we’re focusing on slow mobility, like active travel and micromobility. Individuals who participate in slow mobility are more vulnerable road users, and we want to see how they behave in different travel environments. This can help agencies gain more insights into how to design safety infrastructure. Beyond that I can also envision the technology being used to evaluate whether certain improvements or infrastructure designs help to improve travel satisfaction or improve people’s experience at the same location by doing “before and after” studies. This type of study also allows you to measure and quantify the effect of the improvement.
The use of biometric sensors in the field can also be used to foster meaningful public engagement processes to show the lived experience of different people in a neighborhood or traveling through a different corridor, which can be very powerful.
Q. Do you feel the research methods are at a stage where they are “ripe” for use on other demonstration projects, planning or project development studies?
A. After one year of experimentation, our project team can readily work with biometrics. We have a good understanding of sensor limitations and how to set up the sensors to correctly reduce noise as much as possible. Our experience has also helped determine what kind of metrics can be extracted successfully and reliably through the sensors.
The most useful case for those sensors is to evaluate before and after, so that we can quantify how much people appreciate those implementations in a more accurate way. Beyond that, the sensors can also be effective infrastructure assessment tools. For example, imagine that you ask people to wear biometric sensors and do a bicycle infrastructure evaluation; the agencies can get more realistic and rich data compared with a more traditional survey approach. This rich data can help determine the most effective improvement. It ends up being more inclusive that way.
The tools can be very useful for fostering community engagement with vulnerable populations. For example, if agencies want to improve the accessibility for wheelchair users, they can ask individuals in wheelchairs to wear the sensors and move about an area. Recording and reviewing how they experience a journey is more powerful compared with just asking individuals with needs about their travel patterns. It’s going to be a more straightforward way to show the world how we can make the streets more inclusive for those vulnerable populations.
Q. Do you think local governments and non-governmental organizations could make use of biometrics sensors as a strategy to promote community engagement and outreach to local communities, or to address specific community safety or livability issues? Would it be cost-prohibitive to employ such tools for such community-based planning issues at this time?
A. From my point of view, the most effective way would be for the agencies to identify where there are needs and promising projects and then work with skilled researchers or practitioners who have these sensors already and have begun to climb the learning curve in the use of sensors and interpretation — for example, they could work with us. They would need to pay for the researchers’ time and participation incentives, or if they were to collaborate with a UTC (University Transportation Center) to conduct such research collaboratively.
The sensors are not the most expensive part of the study. The most expensive item is the researcher’s time to collect and analyze the data. The data are very complicated to analyze in the first place because it’s a large amount of data with noises. The researchers need to put in a lot of time to get it to the state where you can extract the relevant variables out and start to interpret them.
Q. How would you characterize the “state-of-training” in using biometrics for students or early career or mid-career professionals in transportation?
A. The biometric sensor itself is not very new, but new to the transportation field, especially for slow modes. It has been widely used in cognitive psychology, where there are classes to interpret those as well. Generally, I don’t think the current transportation and urban planning curriculum for students includes enough classes to cover those sensors. We probably need to teach not only biometric sensors, but urban sensing in general.
In an ideal course, students could get their hands dirty by putting those sensors in the field and then once the data are collected, they can learn how to preprocess and analyze the data. It would have to be a one-year kind of curriculum design to get people involved and ready for it. Of course, instruction on the use of sensors will differ by topic. For example, if you are working in the air quality field, then there are many different air quality sensors and each of them come with different data formats and require different experiment design and analytic skills.
Regarding the mid-career transportation professional, at this moment I believe the research is more in the academic field and focusing on testing and evaluation. I wouldn’t suggest that the research is so ripe that a mid-career transportation or urban planner professional should need to invest their time in learning how to use biosensors unless they have a research project that may benefit substantially from using the sensors.
Resources
To learn more about the use of biometrics in the field of active transportation, see:
Ryerson, M., Long, C., Fichman, M., Davidson, J.H., Scudder, K.N., Kim, M., Katti, R., Poon, G. & Harris, M., (2021). Evaluating Cyclist Biometrics to Develop Urban Transportation Safety Metrics. Accident Analysis & Prevention, Volume 159, 2021. Retrieved from https://www.sciencedirect.com/science/article/pii/S0001457521003183?via%3Dihub
Fitch, D.T., Sharpnack, J. & Handy, S. (2020). Psychological Stress of Bicycling with Traffic: Examining Heart Rate Variability of Bicyclists in Natural Urban Environments. Transportation Research Part F: Traffic Psychology and Behavior, Volume 70, 2020, Pages 81-97. Retrieved from https://www.sciencedirect.com/science/article/pii/S1369847819304073?via%3Dihub.
Zhang. W. 25th Annual NJDOT Research Showcase. Recording starts at: 59:00. Retrieved from https://youtu.be/D_rQP-Dv8gU
Zhang, W., Buehler, R., Broaddus, A. & Sweeney, T. (2021). What Type of Infrastructures do E-scooter Riders Prefer? A Route Choice Model. Transportation Research Part D: Transport and Environment, Volume 94, 2021. Retrieved from https://www.sciencedirect.com/science/article/pii/S1361920921000651.
For more information about the use of biometrics in the broader transportation field, see NYU’s C2SMART’s research project on Work Zone 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 alFigure 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.
Tran, A. (n.d.). Artificial Intelligence-Aided Railroad Trespassing Data Analytics: Artificial Intelligence-Aided Railroad Trespassing Data Analytics:.
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.
All U.S. DOT modes will receive transportation funding from BiL with the greatest amount handled through the Federal Highway Administration (FHWA).
On November 15, 2021, the Infrastructure Investment and Jobs Act (IIJA), often referred to as the “Bipartisan Infrastructure Law” (BIL), was signed into law. With the BIL’s passage, the United States has committed approximately $550 billion to transportation infrastructure within a wider $1 trillion + federal reinvestment in the nation’s infrastructure [1].
Much of the BIL transportation funding seeks to encourage and prioritize the repair, reconstruction and replacement and maintenance of existing transportation infrastructure with appropriations totaling some $350.8 billion (FY 2022-2026), drawing from the highway trust fund ($303.5 billion) and advance appropriations from the general fund (47.3 billion). Most of the highway funding is apportioned to States based on formulas specified in Federal law. New Jersey could receive approximately $8.1 billion over five years for highways and bridges, based on the federal highway funding formula, or about 41.6 percent more than the State’s funding under current law [2]. However, the BiL also provides significant funding through various competitive grant programs such as the bridges and megaprojects that can demonstrate substantial economic benefits. New Jersey’s Portal North Bridge under construction in Secaucus reportedly may meet the requirements for a Capital Investment Grant for transit projects [2].
Most of the highway trust funding is apportioned by formula to the states.
A great deal of the BiL funding being directed for HIPs from the General Fund is formula-based.
Two new climate-focused programs, the Carbon Reduction Program and PROTECT, together match the scale of funding set aside for CMAQ-widening the scope of environmental concerns beyond congestion mitigation and air quality.
Notably, the BIL takes innovative steps in the realms of safety, equity, and climate change and resilience to increase investment and resources for programs, new and old, that will tackle the challenges of the 21st century in both a national and New Jersey-specific context. Growing awareness of the broad harms of road hazards, inequity and injustice, and climate change will inform not only the purpose of specific program investments but influence transportation planning, project delivery, and research for years to come.
Safety
A major program that will advance safety innovation and renovations across the country is the $5 billion, FY 2022-2026 Safe Streets for All (SS4A) Program. A “Complete Streets” program, SS4A is a discretionary program which seeks to advance USDOT’s goal of zero deaths and serious injuries on our nation’s roadways by implementing multi-modal improvements and safety treatments. Examples of applicable SS4A modifications include separated bicycle lanes, traffic calming road design changes, rumble strips, wider edge lines, flashing beacons, and better signage. Metropolitan planning organizations (MPOs), local, and tribal governments are eligible to apply for this funding. Separate provisions in BIL define Complete Streets standards and policies. Additional information on SS4A can be found here. FHWA provides accessible information on Complete Streets here.
A “complete street” in Washington, D.C. with several community livability features for an urban setting such as wide sidewalks with tree coverage, traffic calming design, and a physically protected middle bike lane. Photo by Maria Oswalt on Unsplash.
Changes have been made to existing safety programs such as the Highway Safety Improvement Program (HSIP) which could prove to more holistically mitigate road hazards. Eligibility for HSIP’s funds (up to 10 percent) can now be used for “specified safety projects (including non-infrastructure safety projects related to education, research, enforcement, emergency services, and safe routes to school)” [1]. Definitions for the program have been modified to recognize as eligible a variety of new types of projects such as traffic control devices for pedestrians and bicyclists and “roadway improvements that separate motor vehicles from bicycles or pedestrians” [1]. State-level assessments of vulnerable road users are rolled into the requirements of the HSIP. More information on these guidance changes can be found here.
Funding for highway safety traffic programs under the BIL are $13 billion more than the levels established for the Fixing America’s Surface Transportation (FAST) Act. In FY2022-2026, 402 formula funding for highway safety traffic programs is expected to allocate approximately $42 million to New Jersey to help improve driver behavior and reduce deaths and injuries from motor vehicle-related crashes. This funding represents about a 29 percent increase over FAST Act levels [2] when averaged on an annual basis. Such increases in funding for roadway safety improvement provides an opportunity to put forward educational, enforcement and design strategies to counter a recent surge in US and NJ traffic fatalities.
Equity
To promote and implement equity-oriented innovation, the current administration has held itself to a “Justice40 commitment,” the goal of which is to deliver 40 percent of the benefits of the climate and energy related investments to disadvantaged communities [3]. This commitment is reflected in BIL’s transportation funding. One example provided by USDOT is that $5.6 billion in Low- or No-Emission Bus Grants to transition to low- or zero-emission buses will be assessed and likely partially directed to low-income communities to advance environmental justice.
USDOT developed a definition for disadvantaged communities (DACs) to be utilized in connection with certain criteria under Justice40-covered grant programs. The DAC definition draws upon data for 22 indicators collected at the U.S. Census tract level, which are then grouped into six categories of transportation disadvantage to identify places that are disadvantaged.
The Justice40 Disadvantaged Community Interim Definition goes as follows:
Transportation access disadvantage identifies communities and places where residents spend more, and take longer, to get where they need to go.
Health disadvantage identifies communities based on variables associated with adverse health outcomes, disability, as well as environmental exposures.
Environmental disadvantage identifies communities with disproportionately high levels of certain air pollutants and high potential presence of lead-based paint in housing units.
Economic disadvantage identifies areas and populations with high poverty, low wealth, lack of local jobs, low homeownership, low educational attainment, and high inequality.
Resilience disadvantage identifies communities vulnerable to hazards caused by climate change.
Equity disadvantage identifies communities with a high percentile of persons (age 5+) who speak English “less than well.”
Several USDOT programs are using the interim definition of DACs to ask discretionary grant applicants and formula program administrators to identify how their projects benefit DACs. More information on how the Justice40 commitment shapes the equity orientation of BIL’s transportation funding can be found here.
USDOT’s Disadvantaged Communities map of New Jersey Census Tracts illustrates several places (in yellow) that should inform project planning that is aligned with the Justice40 Commitment
One major new BIL program addressing inequities within America’s transportation infrastructure is the Reconnecting Communities Pilot Program. The discretionary program was conceived to provide $1 billion over five years to remedy the negative effects of past transportation investment decisions that divided communities [1], such as highway expansions that cut cities in half. Applicants for Reconnecting Communities funding can seek capital constructions grants (such as for the replacement of an eligible facility with a new facility that restores community connectivity) or as well as planning grants and technical assistance grants. More information about this innovative program to redress the adverse cumulative effects borne by communities from past transportation investments can be found here.
For New Jersey, the Reduction of Truck Emissions at Port Facilities Program is another innovative program that holds promise for redressing the environmental health effects attributable to siting and operating regional goods movement facilities. By funding the study of, and competitive grants to reduce, truck idling and emissions at ports (such as promotion of port electrification and possibly hydrogen-fuel technologies), pollutants and adverse health disparities borne by port communities could be reduced. Northern New Jersey, as one of the most important freight hubs in North America, is likely to receive some of the $400 million available in discretionary funding (FY2022-FY2026) as well as a portion of the Port Infrastructure Development Program’s annual budget, recently increased to $450 million. These investments to modernize and reduce the environmental burdens of the nation’s freight infrastructure could reduce unfairly distributed health hazards in New Jersey.
In line with the Justice40 commitment, a number of regulatory changes to existing programs contain equity-oriented provisions. In the continuation of the Fixing America’s Surface Transportation (FAST) Act, the Metropolitan Planning Program has a BIL requirement “to consider equitable and proportional representation of population of metropolitan planning area when the MPO designates officials or representatives” [1]. Such requirements, even when non-binding, support a wider culture and consideration of equity in how the nation’s urban and transportation policies are devised and implemented. Many communities today live with the legacy of decisions made without their input, and so this innovative provision in the Metropolitan Planning Program is an appropriate step to discontinue such inequities in institutional processes.
Climate & Resilience
The climate and resilience orientation of the BIL presents innovation not only in fashioning new programs but in integrating carbon reduction goals into existing infrastructure funding frameworks. The newly established Carbon Reduction Program is a formula-funded $6.4 billion addition to the Highway Trust Fund (HTF) for the purpose of backing projects that reduce transportation emissions, and support development of broader carbon reduction strategies. Projects as varied as congestion pricing systems, infrastructure for alternative fueled vehicles (electric, hydrogen, propane, and natural gas), port electrification, replacement of street lighting and traffic control devices with energy-efficient alternatives, and public transportation are eligible. Additional information on the Carbon Reduction Program can be found here.
Charging Station sign: Increased investments in EV charging technology will promote changes in built environment and the energy mix of transportation. Photo by Michael Marais on Unsplash.
Increased need for disaster resiliency in transportation systems informs the purpose of the newly established Promoting Resilient Operations for Transformative Efficient, and Cost-saving Transportation (PROTECT) program. Like the Carbon Reduction Program, PROTECT injects $7.3 billion in the HTF for a formula distribution to the states and also provides $1.4 billion in discretionary funds. This $8.7 billion will help fund resilience improvements in highways, transit systems, intercity passenger rail, and port facilities, as well as support the development of resiliency and evacuation plans. For FY2022 alone, New Jersey is expected to receive roughly $34 million [4] from PROTECT, presenting the opportunity to proactively guard the State’s transportation system from hazards related to climate change. Discussion from the National League of Cities on PROTECT can be found here.
Within the realm of innovative transportation technology, the National Electric Vehicle Infrastructure (NEVI) Formula Program seeks to expand the supply of infrastructure to support the growing presence, if not the transition, of the nation’s fleet to electric and alternative fuel vehicles. Providing approximately $5 billion over five years, NEVI is designed to establish Electric Vehicle (EV) charging stations “along designated Alternative Fuel Corridors, particularly along the Interstate Highway System.” Building on existing federal plans such as Alternative Fuel Corridors, NEVI seeks to guarantee interstate travel by electric vehicle nationally.
Given that New Jersey has the highest number of electric cars per charging station of any state in the country, this additional push is well-suited to the state’s needs and climate goals. The NEVI formula is expected to provide New Jersey with $104.4 million; at an estimated cost per station of $173,000, this level of investment would pay for around 600 charging stations [5]. This funding represents 2.5 percent of the total fund which is roughly commensurate with New Jersey’s Census 2020 population share of 2.8 percent. Another $1.4 billion is available through NEVI discretionary funding that New Jersey could compete to receive.
Similarly, the discretionary Charging and Fueling Infrastructure Program is a competitive funding program with $2.5 billion available to implement innovative fueling infrastructure. At least fifty percent of this funding must be used for a community grant program that prioritizes projects in rural areas, low- and moderate-income communities, and communities with a low ratio of private parking spaces. New Jersey governments’ ability to compete for this funding could shape the built environment and advance the state’s carbon reduction goals for years to come.
Additional information on the National Electric Vehicle Infrastructure Formula Program can be found here. Additional information on how NEVI and the Charging and Fueling Infrastructure program connect within new federal funding programs for EV Charging can found here. An article of NEVI’s role in New Jersey can found here.
Conclusion
FHWA has prepared a table to illustrate how various programs are available to a range of recipients. Interestingly, Safe Streets and Roads for All is the only program that states are not eligible for, conveying a truly neighborhood scale approach.
Other new discretionary programs are established for significant infrastructure programs and freight, equity, planning and project delivery. Research, development, technology and education (RDT&E) program funding levels are authorized with various highway research set-asides established to support deployment and operation of innovative technologies to pilot road usage fees, accelerate digital construction management systems, and advance mobility programs.
The BiL has been characterized as a “once in a generation investment in infrastructure.” As with prior Federal transportation spending bills, the BiL contains provisions that can be expected to influence the nation’s economic competitiveness, environmental sustainability and development priorities. In this case, the BiL offers new opportunities for planning, building, and maintaining a transportation system that is more reliable and safe, equitable, and resilient to economic and energy security challenges and climate change.
What is Next-Generation TIM: Integrating Technology, Data, and Training?
New methods for improving Traffic Incident Management (TIM) programs aim to increase traveler and responder safety and improve trip reliability and commerce movement on all roadways.
Over 6 million reportable crashes occur every year in the United States. Each crash places responders and motorists at risk of secondary crashes while having a severe impact on congestion. New tools, data, and training mechanisms can be used to improve safety and reduce clearance times at roadway crashes. New and existing TIM programs, including those for local agencies and off-interstate applications, will benefit from using enhanced TIM practices on all roadways to save lives, time, and money.
A New Generation of TIM
While the FHWA's national TIM responder training program successfully trained almost 500,000 responders to clear incidents collaboratively, safely, and quickly, it was largely focused on agencies that respond on interstates and high-speed roadways. Next-generation (NextGen) TIM increases the focus on local agency TIM programs while integrating new and emerging technology, tools, and training to improve incident detection and reduce safety response and clearance times on all roadways.
Traditionally, transportation agencies capture incidents (crashes, roadway debris, stalled vehicles on mainlines, etc.) where sensor technologies are installed, where safety service patrols are present, or when contacted by public safety/law enforcement agencies. NextGen TIM significantly expands this capacity. It enables agencies to improve TIM strategies by implementing new options such as back-of-queue warning, navigation-app notification of active responders in the vicinity, notification-based incident detection using crowdsourced data, and more.
By using NextGen TIM methods, State and local agencies can increase traveler and responder safety, improve trip reliability and commerce movement, and enable responder communities to focus more resources on other pressing citizen needs.
Benefits
Increased Safety. NextGen TIM targets advances in safety through engineering, enforcement, education, and emergency services to help keep responders, drivers, and pedestrians safe across freeway, arterial, and multimodal travel.
Improved Travel Times. Training, data, and technology combine to help local and State agencies reduce secondary crashes and clearance times, improving trip reliability and increasing motorists' awareness of active responders along their travel routes.
Improved Operations. Integrating new and emerging technology, tools, and training can improve incident mitigation and safety throughout the whole TIM timeline, from incident detection to clearance on all roadways.
How NJ Incorporates NextGen Traffic Incident Management (TIM)
Stage of Innovation:
DEVELOPMENT
(December 2022)
Research. NJDOT is coordinating with State Police to determine communications that will be shared with Computer-Aided Dispatch (CAD) integration. NJDOT is also working to establish radio channels to enable coordinated DOT and law enforcement communications at incident sites.
Training. NJDOT is actively working towards achieving participation by all local agencies in the NJDOT established statewide TIM training course.
Building Support. DVRPC area-generated incident management task forces can serve as models for creation of similar diverse stakeholder task forces in other regions. NJDOT is also looking to build partnerships with media to facilitate TIM communications.
What’s Next?
For the EDC-6 initiative, the NJDOT initially wanted to focus on CAD integration as one of the major activities in support of the TIM strategic plan. As a result of NJ State Police's decision to change their CAD technology, the NJDOT is revising their approach for EDC-6 NextGen TIM.
NJDOT is continuing to coordinate with the NJIT ITS Resource Center to deploy HAAS Alert technology on NJDOT's Safety Service Patrol vehicles. The responder-to-vehicle alert application will deliver incident alerts to the motorists (i.e. phone apps) for their situational awareness when approaching a stopped SSP vehicle assisting stranded motorists to assist in reducing speed and collision.
The NJSP statewide CAD system (Motorola FLEX) is currently being re-evaluated. The NJDOT will continue to maintain the existing working group/team comprising staff from the Mobility Operations, Mobility Planning/Research, and NJIT ITS Resource Center to provide coordination and strategic planning for the CAD integration project.
Next-Generation TIM: Integrating Technology, Data, and Training: NEW & NOTEWORTHY
NJDOT's Traffic Incident Management training is now available as an online, self-guided course. Bringing first responder training program to online platform should make it ...
We spoke with Sue Catlett from NJDOT's Transportation Mobility, Planning and Research Group to get an update on Crowdsourcing, Weather Responsive Management and Traffic Incident ...
The pilot study continues to examine the effectiveness of connected vehicle technology to alert motorists to Safety Service Patrol (SSP) workers at an incident site. ...
This study will examine the effectiveness of connected vehicle technology to alert motorists to Safety Service Patrol (SSP) workers at an incident site. ...
Reducing fatalities on rural roads remains a major challenge in the United States. Roadway departures on the rural road network account for one-third of traffic fatalities. Systemic application of proven roadway departure countermeasures, such as rumble strips, friction treatments, and clear zones, helps keep vehicles in their travel lanes, reduce the potential for crashes, and reduce the severity of those crashes that do occur.
Data-driven systemic analysis can help agencies prioritize the locations and countermeasures that will be most effective by taking a broad view to evaluate risks across an entire roadway system. It can be used to proactively implement countermeasures where crashes are likely to happen, even for locations where no crashes have been recorded. The benefits include safer roads, quick deployment, and flexibility.
NJ Expands Systemic Application of Proven Safety Countermeasures
Stage of Innovation:
DEVELOPMENT
(January 2021)
With EDC-5, NJ plans to expand their current practices to reduce rural roadway departures:
Utilizes Crash Data for Proactive Systemic Approach. Currently, NJ uses crash data to analyze transportation systems for all public roads and applies a proactive systemic approach including rumble striping, low-cost countermeasure mitigation, high friction surface treatments, and signage improvements, unless a location is on the high crash list. Then NJ provides project-specific mitigation to reduce or eliminate the issue. NJ implements these approaches on rural roads through the Local Safety, High Risk Rural Roads, and Preliminary Engineering and Design Assistance Programs.
What's Next?
Be Proactive and Organize Workshops. The FHWA Resource Center conducted a Train-the-Trainer Workshop at NJDOT Headquarters in June 2019. Training events, hosted by the state's MPOs, were being planned for safety professionals, rural roadway facility owners and maintainers on roadway departures, location identification, systemic approach to safety, and proven safety countermeasures, with the assistance of the FHWA Safety Program Office and the FHWA Resource Center.
The planned in-person workshops were cancelled due to COVID restrictions and CDC/State guidelines. Instead NJDOT and FHWA plan to host virtual training sessions presented by the FHWA Resource Center, tentatively scheduled for March 2021.
Reducing Rural Roadway Departures: NEW & NOTEWORTHY
The FHWA Focus on Reducing Rural Roadway Departures (FoRRRwD) initiative, part of EDC-5, looks to provide systematic, targeted solutions for implementing rural road safety measures. ...
On May 12, the FHWA will host FoRRRwD on All Public Roads: Innovative Mechanisms to Deliver Safety Projects, the second of its monthly webinars designed ...
Reducing fatalities on rural roads is a key challenge for transportation agencies in the United States, where roadway departures on rural networks account for one-third ...
What is Safe Transportation for Every Pedestrian (STEP)?
Pedestrians account for over 17.5 percent of all fatalities in motor vehicle traffic crashes, and the majority of these deaths occur at uncontrolled crossing locations (such as non-intersections) or at intersections with no traffic signal or STOP sign. Cost-effective countermeasures with known safety benefits can help reduce pedestrian fatalities in these scenarios.
FHWA promoted the following safety countermeasures through EDC-4 and EDC-5:
Road Diets can reduce vehicle speeds, limit the number of lanes pedestrians cross, and create space to add new pedestrian facilities.
Pedestrian Hybrid Beacons (PHBs) are a beneficial intermediate option between Rectangular Rapid Flash Beacons (RRFBs) and a full pedestrian signal. They provide positive stop control in areas without the high pedestrian traffic volumes that typically warrant signal installation.
Pedestrian Refuge Islands provide a safe place to stop at the midpoint of the roadway before crossing the remaining distance. This is particularly helpful for older pedestrians or others with limited mobility.
Raised Crosswalks can reduce vehicle speeds.
Crosswalk Visibility Enhancements, such as crosswalk lighting and enhanced signing and marking, help drivers detect pedestrians—particularly at night.
Stage of Innovation:
INSTITUTIONALIZED
(January 2021)
NJ's work on STEP began with EDC-4 and continued to progress during EDC-5:
Developed an Action Plan for Implementing Pedestrian Crossing Countermeasures at Uncontrolled Locations. For this collaborative effort, NJDOT and FHWA reviewed existing practice and policies impacting crossings and recommended actions for targeting specific safety countermeasures to help reduce the number and rate of pedestrian crashes, fatalities, and injuries on NJ highways.
Devised RecommendationsFollowing STEP Guidance for Implementing Lower-Cost Countermeasures. The recommended countermeasures can be deployed based on specific needs, have a proven record of reducing crashes, and represent underutilized innovations that can have an immediate impact.
Developed NJ 2020 Strategic Highway Safety Plan. STEP strategies have been included in the 2020 NJ Strategic Highway Safety Plan update, completed in August 2020 and implementation efforts of proposed actions items are underway.
What's Next?
New Jersey has developed strategies in the 2020 Strategic Highway Safety Plan and will implement these strategies with the goal of eliminating all pedestrian and bicyclist fatalities and serious injuries on all public roads.
Following a decade of transportation planning studies, including a Road Safety Audit (RSA), pedestrian and cyclist improvements are being programmed for Atlantic City's Atlantic ...
Pedestrian Hybrid Beacons, one of FHWA’s seven Safe Transportation for Every Pedestrian (STEP) countermeasures, proven methods of reducing pedestrian collisions, are the subject of a ...
EDC STEP-aligned projects have been successfully deployed in locations across New Jersey, including a recent pedestrian improvement project along Washington Avenue in the Borough of ...
Please join the NJDOT Bureau of Research on April 2nd for an Innovation Exchange Webinar, EDC Safe Transportation for Every Pedestrian (STEP), that we are ...
The New Jersey Local Technical Assistance Program (NJLTAP) has partnered with the FHWA Division Office, NJDOT Bureau of Safety, Bicycle and Pedestrian Programs and Local ...
The following videos describe six of FHWA’s Proven Safety Countermeasures that improve pedestrian safety. NJDOT developed these videos to train and educate viewers on the ...
The NJ Local Technical Assistance Program is holding an all-day workshop training event to learn more about the FHWA EDC-5 innovative initiative: Safe Transportation for ...
The Local Safety Peer Exchange Summary Report describes a series of peer exchange events that highlighted local initiatives that demonstrate best practice in addressing traffic ...
