Testing Biometric Sensors for Use in Micromobility Safety

Posted on April 2, 2024April 3, 2024

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. The eye 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.

To read more on Dr. Zhang’s work, see:

Zhang, W. (2023). Rider-centric Approach to Micromobility Safety. 25th Annual NJDOT Research Showcase. Presentation. Retrieved from https://www.njdottechtransfer.net/wp-content/uploads/2023/11/Zhang-Safety-2nd-Presentation.pdf.

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:

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

Posted on February 9, 2024February 12, 2024

The State of New Jersey has advanced policies, programs, and projects such as complete streets that encourage greater transportation modal choice and less dependency on the single-occupancy vehicle to address climate change and promote mobility and access for all users. The NJ Statewide Bicycle and Pedestrian Master Plan and Complete Streets policy seek to increase connectivity and micromobility safety through road diets and other infrastructure changes. New Jersey Department of Transportation (NJDOT) is also sponsoring safety programs that aim to reduce traffic related fatalities. In-depth research that looks at differences across gender, race, socioeconomic status and ability may assist in expanding policies and statewide initiatives that prioritize safety and accessibility for all road users.   

This Q&A article has been prepared following an interview with Dr. Hannah Younes, a post-doctoral researcher at the Alan M. Voorhees Transportation Center at Rutgers University, who focuses on cyclists and e-scooter behavioral patterns. Her most recent study conducted in Asbury Park, NJ observed gender differences of chosen micromobility mode, helmet usage, and location. We interviewed her to better understand the data sources and methods used in the research, its limitations and explore the applicability and implications of her research findings to the field of transportation and future research. The Q&A interview has been edited for clarity.

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?   

Motor-vehicle travel is the most polluting transportation mode. A shift towards micromobility Infrastructure, particularly in cities, can help induce travel from eco-friendly modes and reduce motor-vehicle use. This should be in conjunction with other measures, such as increasing gasoline prices, increasing parking costs, and providing subsidies for eco-friendly modes.

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 to Implementation: The Use of Porous Concrete in Sidewalks

Posted on November 14, 2023November 15, 2023

This Research to Implementation video presents an example of NJDOT-sponsored research and the effect such research has in addressing transportation-related issues within the State.

Pervious (or porous) concrete has been gaining popularity as a potential solution to reduce the amount of impermeable surfaces associated with sidewalks, reduce puddling, and potentially slow storm water surface runoff. As important as these benefits are to surface runoff mitigation, concerns exist as to the ability of pervious concrete to provide sufficient structural support and longevity for the expected service life of the sidewalks as well as its life cycle costs. The composition of pervious concrete can limit its mechanical strength and present challenges in its maintenance to achieve the expected service life.

With support from NJDOT’s Bureau of Research, researchers have looked at the benefits and challenges to utilizing porous concrete for sidewalks, and conducted a follow-up demonstration project. For more information about this research and the demonstration project, see: The Use of Porous Concrete for Sidewalks and Implementation of Porous Concrete for Sidewalks in New Jersey.

The Research to Implementation video series promotes the benefits of funded research to increase the safety of the traveling public, reduce costs, and increase efficiency.

The NJ Transportation Ideas Portal is Open to Your Ideas!

Posted on July 21, 2023July 21, 2023

The New Jersey Department of Transportation’s (NJDOT) Bureau of Research invites you to share your research and innovation ideas on the NJ Transportation Ideas Portal.

We seek to fund RESEARCH IDEAS that lead to implementation – to the testing and adoption of new materials and technologies, to better specifications and to greater efficiency. We strive to discover and advance feasible solutions for more durable infrastructure, greater environmental protection and resilience, and improved mobility and safety for residents, workers and visitors.

We encourage you to suggest INNOVATION IDEAS. We seek to find strategies to advance deployment of innovations and knowledge transfer in transportation. We work with the New Jersey State Transportation Innovation Council (NJ STIC) whose mission is to identify, evaluate, and where possible, rapidly deploy new technologies and process improvements that will accelerate project delivery and improve the quality of NJ’s transportation network. Innovation Ideas will be vetted for next steps which might include research or supporting an initiative to deploy a new technology or process improvement to accelerate innovation.

WHO CAN SUBMIT IDEAS? NJDOT’s research customers and other interested transportation practitioners are encouraged to submit a research or innovation idea. The portal should be of interest to NJDOT, MPOs, county and local governments, and other transportation subject matter experts from university, industry and trade organizations and other NGOs. The portal is also open to the public.

WHO ARE RESEARCH CUSTOMERS? Subject matter experts from NJDOT, NJ TRANSIT, or the NJ Motor Vehicles Commission are often our research customers. Research ideas typically must have a champion among our research customers. Ideally, a “champion” is a responsible individual within a division, bureau or unit who is prepared to sponsor or advance a research idea from its inception to study completion.

COLLECTING IDEAS NOW! Our research and innovation teams review submitted ideas for possible funding and other actions throughout the year. The last day to submit research ideas for the next round of funded transportation research is December 31, 2023.

REGISTER TO PARTICIPATE AND SUBMIT AN IDEA. Once you are registered, you may submit ideas at any time. If you registered previously, you should not need to register again. Click on the “+” button at the top of the page to register. Only registered participants may submit a new idea or vote on other ideas to show your support. Register at the NJ Transportation Ideas here: https://njdottechtransfer.ideascale.com/

QUESTIONS ABOUT HOW TO REGISTER?
Email: ideas@njdottechtransfer.net

For more information about NJDOT Bureau of Research, visit our website: https://www.state.nj.us/transportation/business/research/

Or contact us: research.bureau@dot.nj.gov or (609) 963-2242

Research Spotlight: NJ Transit Grade Crossing Safety

Posted on April 19, 2023April 19, 2023

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%).

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

Share Your Ideas on the NJ Transportation Research Ideas Portal!

Posted on September 1, 2022August 26, 2022

The New Jersey Department of Transportation’s (NJDOT) Bureau of Research invites you to share your ideas on the NJ Transportation Research Ideas Portal.

We are asking NJDOT’s research customers and other transportation stakeholders to propose research ideas for the NJDOT Research Program. Join us in finding workable solutions to problems that affect the safety, accessibility, and mobility of New Jersey’s residents, workers, visitors and businesses.

REGISTER TO PARTICIPATE. Once you are registered, you may submit ideas at any time. If you registered previously, you should not need to register again. Click on the “+” button at the top of the page to register.

HOW DO I SUBMIT AN IDEA? Only registered participants can log in to submit a new idea or vote on other ideas to show your support. Register at the NJ Transportation Research Ideas here: https://njdottechtransfer.ideascale.com/

MORE INFO. Our Welcome and FAQs page offers more information.

NEXT ROUND OF RESEARCH. Submit your research ideas no later than December 31, 2022 for the next round of research RFPs. The NJDOT Research Oversight Committee (ROC) will prioritize research ideas after this date, and high priority research needs will be posted for proposals.

Questions about how to register?
Email: ideas@njdottechtransfer.net

For more information about NJDOT Bureau of Research, visit our website: https://www.state.nj.us/transportation/business/research/

Or contact us: Bureau.Research@dot.nj.gov or (609) 963-2242

Zone for AI to look for trespassing at railroad crossing

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

Posted on August 19, 2022August 22, 2022

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.

NJDOT UAS/Drone Procedures Manual and Best Practices for Use in New Jersey

Posted on December 9, 2021March 4, 2022

The use of drones at NJDOT has expanded to improve safety and efficiency and save time and money.

The NJDOT Knowledge Management Toolbox offers examples of several knowledge sharing practices that have been, or could be, adopted by agency units to retain knowledge in a unit in the face of illness, retirements or transfers to other units at NJDOT.

NJDOT’s Unmanned Aircraft Systems Flight Operations Manual (UASFOM) is an example of knowledge sharing through development of a procedures manual that guides practice within the agency. In 2021, Anil Agrawal, PhD., a Professor of Engineering at The City College of CUNY, completed a research study, NJDOT UAS/Drone Procedures Manual and Best Practices for Use in New Jersey, funded through the NJDOT’s Bureau of Research. The study resulted in the UASFOM that standardizes all aspects of UAS operations for NJDOT’s use, and provides guidance to NJDOT personnel, consultants, and contractors for the inspection, operation, and management of UAS. The document emphasizes maintaining a high level of safety standards in daily flight operations while meeting performance targets.

NJDOT’s Bureau of Aeronautics has used drones to video NJDOT dredging operations, among other applications.

Unmanned Aerial Systems (UAS), or drones, were promoted by the Federal Highway Administration (FHWA) as one of the Every Day Counts Round 5 (EDC-5) innovations. In May 2016, the New Jersey Department of Transportation’s Division of Multimodal Services established the Unmanned Aircraft Systems (UAS) Program as a unit within the Bureau of Aeronautics. Under the direction of NJDOT’s UAS Coordinator, Glenn Stott, NJDOT became a national leader in UAS. Mr. Stott retired from the agency in 2021.

NJDOT Bureau of Aeronautics used several funding grants to build the program and purchase equipment and provide training. Integrating UAS in transportation has been the subject of research and field studies to demonstrate the use case for high-mast light pole inspections, traffic incident management and monitoring, dredging and beach replenishment, photogrammetry, bridge inspection, and watershed management, among other topics. UAS has been shown to improve safety, save time and money and increase efficiency. UAS is considered to be institutionalized within NJDOT.

An example Risk Management Worksheet is one of several forms described in the Procedures Manual.

The procedures manual provides comprehensive guidance for UAS missions from planning to debriefing. The manual presents NJ’s laws and regulations affecting UAS operations, discusses NJDOT’s safety management system and risk management approach, established best practices, the agency’s three-phase training program, and incident reporting. The manual also provides NJDOT’s UAS forms needed for documentation and to ensure compliance with Federal Aviation Administration (FAA) regulations. The manual is intended to be a “living document” to incorporate changes as experience grows with UAS within the agency.

A procedures manual is one way to counter the loss of expertise and institutional knowledge when employees retire or transfer. A manual can build and sustain knowledge within the agency to ensure continuity of operations.

The UASFOM can be found in the Knowledge Management Toolbox. The Final Report and Technical Brief for the Research can be accessed here.

Research Spotlight: Evaluating the Pedestrian Hybrid Beacon’s Effectiveness: A Case Study in New Jersey

Posted on August 24, 2021August 24, 2021

A Pedestrian Hybrid Beacon (PHB) is a signalized, pedestrian-activated device designed to increase crossing safety. A recent study conducted by the New Jersey Bicycle and Pedestrian Resource Center (BPRC), funded by NJDOT, examined the efficacy and public awareness of PHBs in New Jersey. The authors, researchers from Rowan and Rutgers universities, found a persistent need to better educate motorists and pedestrians in New Jersey on the PHB and its phases.

The five phases Pedestrian Hybrid Beacon’s (PHB) operations

Pedestrian Hybrid Beacons are one of FHWA’s seven Safe Transportation for Every Pedestrian (STEP) countermeasures, proven methods of reducing pedestrian collisions. STEP was promoted through multiple rounds of the FHWA’s Every Day Counts (EDC) Program. A PHB is typically placed to improve pedestrian safety at uncontrolled and mid-block crossings, in locations with high pedestrian demand and wide roadways. The treatment consists of two signal arms on each side, with pedestrian “push buttons” and a crosswalk. The PHB operates in five phases. In the first, the PHB’s signal is off. The second phase begins when a pedestrian activates it by pressing a button, prompting the signal to flash a yellow light. Then, for the third phase, the flashing transitions to a solid yellow light, communicating to drivers that they should prepare to stop. Then the light turns red, and, in the fourth phase, the pedestrian signal changes to “Walk.” After an interval, the fifth phase begins: the pedestrian signal displays a countdown timer, and the traffic signal flashes alternating red lights, telling drivers to stop and that they may proceed if the crosswalk is clear.

The study’s literature review found multiple examples of prior research demonstrating the efficacy of PHBs. In the case of Tucson, Arizona, where one of the first PHBs was deployed in the United States, one study found a 69 percent decrease in pedestrian-related crashes in the signal area. Another analysis in Tucson found a 97 percent yielding rate from drivers at PHB-equipped crossings. One of the chief findings from the literature review was that PHB signal evaluations were lacking in New Jersey. Thus, researchers aimed to more systematically analyze PHBs in the state.

The authors found ten implemented examples of PHBs throughout the state, from Bergen County to Atlantic County. For more in-depth research, they selected signals in three different community types (urban, suburban, and campus area), in Morristown, Medford, and New Brunswick, New Jersey, to undergo video analysis.

The five phases Pedestrian Hybrid Beacon’s (PHB) operations

One commonality observed in all three locations was an apparent confusion for motorists concerning the fifth phase, in which the signal flashes red, indicating that drivers should stop and then proceed with caution. In New Brunswick, 100 percent of observed motorists remained stopped, even after the intersection had been cleared. In Morristown, the vast majority of pedestrians (91.3%) failed to use the PHB during the morning period, and also failed to do so in the evening (83%). The authors attribute such behavior to the PHB timing being linked to two nearby traffic signals, contributing to extra delay after the crossing button has been pressed. When inconvenient, it seems, pedestrians may opt to cross on their own.

To better understand the familiarity of pedestrians and motorists in New Jersey with PHBs, the researchers designed an online survey that was sent to 79,567 randomly selected email addresses from 30 communities across the state. While respondents indicated some confusion as to how PHBs functioned, a plurality indicated that they would be very likely or somewhat likely to support implementation in their own community. A majority of respondents (85.9%) reported that they had never heard of PHBs, and later indicated that completing the short survey had increased their knowledge of the safety treatment, showing the potential benefit of more public education about their functionality.

The report concludes by stating that while PHBs are proven to be effective at increasing pedestrian crossing safety, a lack of public awareness on the part of both drivers and pedestrians currently limits the effectiveness of these devices. The researchers suggest updating the New Jersey Motor Vehicle Commission’s Driver’s Handbook to include the PHB, and to differentiate the flashing red signals at a PHB where the driver must yield and then proceed if the crosswalk is clear, from the flashing red signals at railroad crossings where the driver is required to stop and remain stopped. This addition could be complemented with a public education campaign to teach pedestrians and drivers about the intricacies of Pedestrian Hybrid Beacons.

The New Jersey Bicycle and Pedestrian Resource Center (BPRC) works to promote a safer and more accessible walking and bicycling environment in the state. The Center, located at the Alan M. Voorhees Transportation Center at Rutgers, is supported by NJDOT through funding from FHWA. Further information technical assistance, resources for Complete Streets, and current research is available on the BPRC’s website.

Resources

Federal Highway Administration. Pedestrian Hybrid Beacons. Federal Highway Administration. https://safety.fhwa.dot.gov/provencountermeasures/ped_hybrid_beacon/

New Jersey Bicycle and Pedestrian Resource Center. (2020). Evaluating the Pedestrian Hybrid Beacon’s Effectiveness: A Case Study in New Jersey. New Jersey Bicycle and Pedestrian Resource Center. http://njbikeped.org/portfolio/evaluating-pedestrian-hybrid-beacons-effectiveness/

NJDOT Tech Transfer. (2019). What is a Pedestrian Hybrid Beacon? NJDOT Tech Transfer. Video. https://www.njdottechtransfer.net/2019/09/27/njdot-safety-countermeasures-videos/

NJDOT Tech Transfer. (2020). STEP-Aligned HAWK Signal Installed in Bergen County. NJDOT Tech Transfer. https://www.njdottechtransfer.net/2020/03/20/step-aligned-hawk-signal-installed-in-bergen-county/

Research to Implementation: Design and Evaluation of Scour for Bridges Using HEC-18

Posted on June 10, 2021June 10, 2021

This Research to Implementation video presents an example of NJDOT-sponsored research and the effect such research has in addressing transportation-related issues within the State.

Bridge scour is the removal of sediment such as sand and gravel from around non-tidal bridge substructures and supports caused by swiftly moving water. This water can scoop out scour holes, compromising the integrity of a structure. Understanding the extent of bridge damage and prioritizing the order of repair is critical to maintaining safe bridges.

With the support of NJDOT's Bureau of Research, researchers developed the NJ-specific Scour Evaluation Model (SEM) to prioritize bridges for repair. The SEM model was determined to be effective and is now approved by FHWA and NJDOT to evaluate scour risk. The project included training of consultants to encourage the expanded use of the SEM model in NJ.

The video promotes the benefits of funded research to increase the safety of the traveling public, reduce costs, and increase efficiency.