Category Archives: Research Library

Did You Know? TRID Searches, Lighting, and Recent NJ Research

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The NJDOT Technology Transfer Research Library offers valuable resources, including the TRID database, which helps researchers access transportation publications by topic, keyword, or geographical area. TRID can serve as a valuable tool to expand knowledge on innovations in topics such as lighting, or to learn more about local research.

The NJDOT Technology Transfer Research Library page features a “Did You Know” page that provides key information about the library, transportation research resources, as well as newly released publications available through AASHTO and the ASTM COMPASS Portal. Additionally, the site hosts a TRID Searches page, offering a list of recent publications indexed in the Transport Research International Documentation (TRID) database, categorized into 37 subject areas. The TRID database features specialized search options allowing researchers and other interested parties to locate publications using geographical, subject area, and key term identifiers.

An emergency response truck that has a detachable and extendable lighting tower equipped on the back of the vehicle.
Example of the tower lighting equipped on NJDOT emergency response vehicles. Courtesy of NJDOT

NJDOT frequently advances innovative transportation projects across various research topics, including lighting initiatives under the FHWA’s Every Day Counts (EDC-7) program. In one example, NJDOT collaborated with Rutgers-VTC, and Rowan University to produce a pedestrian lighting draft report, as part of the Nighttime Visibility for Safety initiative. The research team determined optimal lighting levels and designed pedestrian lighting infrastructure to improve safety. The researchers presented project findings at the 2024 NJDOT Research Showcase, with a full report expected in 2025. Additionally, NJDOT advanced innovations in nighttime traffic incident management through the procurement of lighting towers and LED flares for emergency response vehicles, as part of the EDC-7 Next-Generation Traffic Incident Management (NextGen TIM): Technology for Saving Lives initiative.

As NJDOT advances its lighting innovations, the TRID database can serve as a valuable resource to explore similar lighting-related research and initiatives both nationally and within New Jersey. A search of the TRID database using the keyword “lighting” uncovers hundreds of recent transportation studies that focus on or incorporate lighting. One such study explored ways to enhance the safety of winter road maintenance vehicles, such as snowplows, by identifying the most effective vehicle lighting to improve reaction times. Another examined racial and poverty-level disparities in pedestrian nighttime crashes, highlighting the increased crash risk in low-income and minority communities due to inadequate lighting and pedestrian infrastructure.

A work vehicle installing steel electrodes to a length of road in New Jersey.
Installation of steel electrodes in the asphalt assessment. Marath. A., A. Saidi, A. Ali, and Y. Mehta. (2024)

In addition to researching specific topics, the TRID database can be used to locate publications by geographical area. Using “New Jersey ” as a keyword uncovers studies that focus on local transportation research and innovations. For instance, one study evaluated the performance of conductive asphalt pavements in the state, finding that a high-performance thin overlay (HPTO) asphalt mixture with graphite and carbon fibers offered the best cracking resistance. Another study, sponsored by NJ TRANSIT, examined factors contributing to the decline in bus ridership, identifying major contributors like infrequent service and a lack of direct connections to key destinations.

TRID Database

Lighting-Based Research

Lighting-based research can be found on the TRB TRID database. Below are several recent national transportation research articles on lighting:

Belloni, E., C. Buratti, L. Lunghi and L. Martirano. (2024). A new street lighting control algorithm based on forecasted traffic data for electricity consumption reduction. Lighting Research and Technology. Vol. 56. https://trid.trb.org/View/2248974

Dubey, S., A. Bailey, and J. Lee. (2025). Women’s perceived safety in public places and public transport: A narrative review of contributing factors and measurement methods. Cities. Vol. 156. https://trid.trb.org/View/2447605

Kidd, D., L. Riexinger, and D. Perez-Repela. (2024). Pedestrian automatic emergency breaking responses to a stationary or crossing adult mannequin during the day and night. Traffic Injury Prevention. Vol. 25. https://trid.trb.org/View/2452794

Li. H., L. Wang, and M. Yang. (2025). Collaborative effects of vehicle speed and illumination gradient at highway intersections exits on drivers’ stress capacity. Accident Analysis & Prevention. Vol. 209. https://trid.trb.org/View/2447380

Mwende, S., V. Kwigizile, and J. Oh. (2024). Investigating Racial and Poverty-Level Disparities Associated with Pedestrian Nighttime Crashes. Transportation Research Record: Journal of the Transportation Research Board. Vol. 2678. https://trid.trb.org/View/2361845

Ouyang, H., P. Liu and Y. Han. (2025). Exploring Factors Contributing to Pedestrian Injury Severity in Pedestrian-Vehicle Crashes: An Integrated XGBoost-SHAP, Latent Cluster, and Mixed Logit Approach. Journal of Transportation Engineering, Part A: Systems. Vol. 151. https://trid.trb.org/View/2479744

Rangaswamy, R., N. Alnawmasi, and Z. Wang. (2024). Exploring contributing factors to improper driving actions in single-vehicle work zone crashes.: A mixed logit analysis considering heterogeneity in means and variances, and temporal stability. Journal of Transportation Safety & Security. Vol. 16. https://trid.trb.org/View/2399835

Van Beek, A., Y. Fang and D. Duives. (2024). Studying the impact of lighting on the pedestrian route choice using Virtual Reality. Safety Science. Vol. 174. https://trid.trb.org/View/2345069

Vidal-Tortosa, E. and R. Lovelace. (2024). Road lighting and cycling: A review of the academic literature and policy guidelines. Journal of Cycling and Micromobility Research. Vol. 2. https://trid.trb.org/View/2334660

Wong, A. D. Sharma, F. Momeni, and S. Wong. (2025). Naturalistic Experiment for Surface Transportation: A Study of Snowplow Lighting Under Winter Conditions. Journal of Transportation Engineering, Part A: Systems. Vol. 151. https://trid.trb.org/View/2464993

New Jersey-Based Research

New Jersey-based research can also available through the TRB TRID database. Below are several recent articles on New Jersey transportation research:

Assaad, H., M. Mohammadi, and G. Assaf. (2024). Determining Critical Cascading Effects of Flooding Events on Transportation Infrastructure Using Data Mining Algorithms. Journal of Infrastructure Systems. Vol. 30. https://trid.trb.org/View/2373908

Devajyoti, D., and C. Wang. (2024). An investigation into the potential use of information and communication technologies by trip-deprived older adults in New Jersey. Transportation Research Part A: Policy and Practice. Vol. 188. https://trid.trb.org/View/2415346

Devajyoti, D., and Z. Liu. (2024). Who stopped riding buses and what would motivate them to return? A New Jersey case study. Case Studies on Transport Policy. Vol. 15. https://trid.trb.org/View/2343481

Hasan, A.S., M. Jalayer, S. Das and M. Bin Kabir. (2024). Application of machine learning models and SHAP to examine crashes involving young drivers in New Jersey. International Journal of Transportation Science and Technology, Vol. 14. https://trid.trb.org/View/2162338

Keenan, K. (2024). The transportation policy elite and their ladder of citizen participation: Problems and prospects around communication methods in New Jersey. Cities. Vol. 145. https://trid.trb.org/View/2309380

Khameneh, R., K. Barker, and J. Ramirez-Marquez. (2025). A hybrid machine learning and simulation framework for modeling and understanding disinformation-induced disruptions in public transit systems. Reliability Engineering & System Safety. Vol. 255. https://trid.trb.org/View/2465146

Marath. A., A. Saidi, A. Ali, and Y. Mehta. (2024). Assessment of mechanical performance of electrically conductive asphalt pavements using accelerated pavement testing. International Journal of Pavement Engineering. Vol. 25. https://trid.trb.org/View/2487585

Najafi, A., Z. Amir, B. Salman, P. Sanaei, E. Lojano-Quispe, A. Maher, and R. Schaefer. (2024). A Digital Twin Framework for Bridges. ASCE International Conference on Computing in Civil Engineering 2023, American Society of Civil Engineers, pp 433-441. https://trid.trb.org/view/2329319 

Patel, D., R. Alfaris, and M. Jalayer. (2024). Assessing the effectiveness of autism spectrum disorder signs: A case study in New Jersey. Transportation Research Part F: Traffic Psychology and Behaviour. Vol. 100. https://trid.trb.org/View/2293015

Zaman, A., Z. Huang, W. Li, H. Qin, D. Kang, and X. Liu. (2024). Development of Railroad Trespassing Database Using Artificial Intelligence. Rutgers University, New Brunswick, Federal Railroad Administration, 80p. https://trid.trb.org/view/2341095 

DYK: National Transportation LibGuides

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Did you know...

National Transportation LibGuides

Librarians from the National Transportation Library (NTL) and members of the National Transportation Knowledge Network (NTKN) produce research e-guides called “National Transportation LibGuides” from time-to-time. These LibGuides provide introductions, summaries, resources, and contact information for various transportation research topics.

Some examples include: Bicycle and Pedestrian Injuries by Type of Vehicle, Knowledge Management in Transportation, Life Cycle of Pavement, Practical Design, and Unmanned Aerial Vehicles. Additionally, some LibGuides cover general research information such as Accessibility, Citation Guides, Digitization, and Research Tools.

In addition to the currently published LibGuides, there are other LibGuides that the National Transportation Library has assigned as private. These guides cannot be found at the above link, but they can still be accessed on the LibGuides website through this document.

DYK: Legal Research Resources

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Did you know...

New Jersey Legal Research

NJDOT employees conducting research on legal issues related to transportation can access many resources via the New Jersey State Library (NJSL).

NJSL’s Law Library recently published a list of New Jersey Legal Research Resources. The Law Library also maintains links to pages related to the State Legislature, administrative law including a searchable database of the New Jersey Administrative Code, and other topics.

NJSL and the NJDOT Research Library also have access to the Lexis Plus database.  This tool allows you to easily search the statutes or administrative code using citations or keywords.  The database includes annotations, case law materials, additional historic information, and links to additional and related information.

The Law Library recently helped an NJDOT employee access a historical version of the State Highway Access Management Code.

To start your research, please contact the NJDOT research librarian, Eric Schwarz, MLIS, at (609) 963-1898, or email library@dot.nj.gov.

Did You Know? AASHTO Publications Available Electronically

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

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

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

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

Did You Know? AI in Transportation

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

AI on Display at the 2024 Research Showcase  

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

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

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

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

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

Poster by Indira Prasad from the 2024 NJDOT Research Showcase.

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

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

Deployment of AI in Programs and Project Implementation  

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

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

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

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

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

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

Visual example of how LiDAR senses the surrounding environment.

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

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

Recent National Research  

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

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

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

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

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

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

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

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

TRID Database 

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

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

Additional Resources

DYK: Inter-Library Loans

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Did you know...

As state employees, NJDOT users are eligible to register for a library card (borrower card) from the New Jersey State Library (NJSL). The card will allow the user to borrow material from the NJDOT Research Library or NJSL.

NJDOT users may request materials from the New Jersey State Library (NJSL) in several ways:

NJSL materials may be picked up at the NJSL in Trenton or at the NJDOT Library in the Main Office Building in Ewing. Materials may also be sent directly to the user’s office upon request.

A library card is also needed to request “interlibrary loan” work-related books and articles from U.S. libraries other than NJDOT or NJSL.

For instructions on requesting interlibrary loan materials online, see the NJSL website. New or technical materials, textbooks, and examination/test books may not be available via interlibrary loan.

The NJDOT Librarian can help users determine if the material will likely be available via interlibrary loan and can help request the item for the user. NJSL staff will keep the user updated on the status of the request, or the user can check the ILLiad portal.

People searching for information online. AASHTO and TRID logos.

Did You Know? AASHTO and TRID Resources

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The New Jersey Department of Transportation (NJDOT) Research Library offers valuable assistance in supporting various research tasks and for accessing resources. This article highlights recent publications from the American Association of State Highway and Transportation Officials (AASHTO), as well as publications indexed in the TRID database. For a background on AASHTO standards and the TRID database, please see our earlier “Did You Know?” article.

AASHTO Publications

Two recent AASHTO publications of note are a print version of the 11th edition of the Manual on Uniform Traffic Control Devices for Streets and Highways (MUTCD) and the 2024 edition of AASHTO’s “Materials Standards” (published electronically).

The 11th edition of the MUTCD was published by the Federal Highway Administration (FHWA) in December 2023 and is available as a free PDF download. It supersedes the previous 10th edition, published in 2009.

The new edition is also available in print format from the NJDOT Library, indexed and printed by AASHTO, in association with the American Traffic Safety Services Association and the Institute of Transportation Engineers. Users must register for a New Jersey State Library card in order to borrow any materials.

On July 31, AASHTO released the Standard Specifications for Transportation Materials and Methods of Sampling and Testing, and AASHTO Provisional Standards, 44th Edition—2024, commonly referred to as the AASHTO “Materials Standards.” These standards contain specifications, recommended practices, and test methods commonly used in the construction of highway facilities. Provisional standards are also published to allow practitioners to use them early in the research or development phase. In addition to revisions to harmonize industry standards, update technology, and generally improve the standards, the 44th edition includes 15 conversions to dual units and more updates to temperature-measuring devices.

The Materials Standards are available in three files on the Research Library’s SharePoint site. In addition to those standards, the following AASHTO reports and standards, published in 2024, are available to NJDOT employees upon request:

  • 2022 AASHTO Salary Survey (Excel and PDF files).
  • 2022 Annual AASHTO State DOT HR Metrics Report.
  • 2024 Interim Revisions – Manual for Bridge Element Inspection 2nd Edition 2019.
  • 2024 Interim Revisions to the LRFD Steel Bridge Fabrication Specifications 1st Edition February 2023.
  • AASHTO 2024 Interim Revisions to the AASHTO/AWS D1.5M/D1.5: 2020 Bridge Welding Code, 8th Edition.
  • AASHTO LRFD Bridge Construction Specifications – 4th Edition – 2024 Interim Revisions.
  • Commuting in America The National Report on Commuting Patterns and Trends—Brief 24.5. Machine Learning Approaches for Populations’ Hard-to-Capture Commuting Behavior.
  • Commuting in America The National Report on Commuting Patterns and Trends—Brief 24.6. Change and Variation in Mode Choice.
  • Guide for Accommodating Utilities within Highways and Freeways – 1st Edition – 2024.
  • Guide Specifications for Structural Design with Ultra-High Performance Concrete – 1st Edition.
  • Guidelines for Field Repairs and Retrofits of Steel Bridges, 1st Edition, G14.2-2023.
  • Movable Bridge Inspection Evaluation and Maintenance Manual – 2nd Edition – 2024 Interim Revisions.
  • Resources for Concrete Bridge Design and Construction – 1st Edition.
  • Survey of State Funding for Public Transportation – Final Report 2024 Based on FY2022 Data.
  • Uniform Audit and Accounting Guide for Audits of Architectural and Engineering (AE) Consulting Firms, 2024 Edition

TRID Database Search

The Research Library has compiled a brief scan of the TRID database search on the reduction of emissions of greenhouse gases and carbon. There is an extraordinary growth in projects underway and recently completed research in transportation, covering a range of policy, planning, environment, materials, construction, multi-modal operations, and vehicle equipment, fuels and technology areas. Selected results from the past 6-12 months, focusing on surface transportation in the United States, are listed here:

Select Projects Underway

Effect of Carbon-Negative Carbon Black on Concrete Properties
https://trid.trb.org/View/2389221

Evaluating Carbon Reduction in Project Selection and Planning
https://trid.trb.org/View/2329694

Shaping Automated Vehicle Behaviors in Mixed Autonomy Traffic to Benefit All Road Users and Reduce Greenhouse Gas Emissions
https://trid.trb.org/View/2350815

Advancing Methods to Evaluate Greenhouse Gas Emissions During Transportation Decision Making and Performance Management
https://trid.trb.org/View/2381725

Shifting Gears to Sustainability: A Deep-Dive into Solar-Powered Bike Pathways
https://trid.trb.org/View/2373992

Advancing Active Transportation Project Evaluation
https://trid.trb.org/View/2313957

Impacts of Remote/Hybrid Work and Remote Services on Activity and Transportation Patterns
https://trid.trb.org

Select Recently Published

Policy

U.S. Department of Transportation. DOT Report to Congress: Decarbonizing U.S. Transportation. July 2024. https://trid.trb.org/View/2404234

Planning

Mullin, Megan; Feiock, Richard C; Niemeier, Deb. Climate Planning and Implementation in Metropolitan Transportation Governance. Journal of Planning Education and Research, Volume 44, Issue 1, 2024, pp 28-38. https://trid.trb.org/view/1936743

Environment

Jeong, Minseop, Jeehwan Bae, and Gayoung Yoo. “Urban roadside greenery as a carbon sink: Systematic assessment considering understory shrubs and soil respiration.” Science of the Total Environment 927 (2024). https://trid.trb.org/View/2377597

National Academies of Sciences, Engineering, and Medicine. “Considering Greenhouse Gas Emissions and Climate Change in Environmental Reviews: Conduct of Research Report.” (2024). https://trid.trb.org/View/2404015

Kelly, Jarod C., Taemin Kim, Christopher P. Kolodziej, Rakesh K. Iyer, Shashwat Tripathi, Amgad Elgowainy, and Michael Wang. Comprehensive Cradle to Grave Life Cycle Analysis of On-Road Vehicles in the United States Based on GREET. No. 2024-01-2830. SAE Technical Paper, 2024. https://trid.trb.org/View/2367212

Ashtiani, Milad Zokaei, Monica Huang, Meghan C. Lewis, Jordan Palmeri, and Kathrina Simonen. “Greenhouse Gas Emissions Inventory from Roadway Construction: Case Study for the Washington State Department of Transportation.” Transportation Research Record (2024). https://trid.trb.org/View/2352361

Zuzhao Ye, Nanpeng Yu, Ran Wei. Joint planning of charging stations and power systems for heavy-duty drayage trucks, Transportation Research Part D: Transport and Environment, Volume 134, 104320 (2024). https://trid.trb.org/View/2408518

Materials and Construction

Lopez, Sarah, Lawrence Sutter, R. Douglas Hooton, Thomas Van Dam, Allison Innis, and Kevin Senn. “Breaking Barriers to Low Carbon Concrete Pavements.” Transportation Research Record (2024). https://trid.trb.org/View/2387006

Equipment, Fuels and Technology

Dugoua, Eugenie; Dumas, Marion. Coordination dynamics between fuel cell and battery technologies in the transition to clean cars. Proceedings of the National Academy of Sciences, Volume 121, Issue 27, 2024, e2318605121. https://trid.trb.org/view/2399820

Jung, Philipp Emanuel; Guenthner, Michael; Walter, Nicolas. Guided Port Injection of Hydrogen as an Approach for Reducing Cylinder-to-Cylinder Deviations in Spark-Ignited H2 Engines – A Numerical Investigation. SAE Technical Paper, 2024. https://trid.trb.org/view/2397724

Wallace, Julian; Mitchell, Robert; Rao, Sandesh; Jones, Kevin; Kramer, Dustin; Wang, Yanyu; Chambon, Paul; Sjovall, Scott; Williams, D. Development of a Hybrid-Electric Medium-HD Demonstrator Vehicle with a Pent-Roof SI Natural Gas Engine. SAE Technical Paper, 2024. https://trid.trb.org/view/2397750

Wine, Jonathan; Ahmad, Zar Nigar; McCarthy, Jr., James; Prikhodko, Vitaly; Pihl, Josh; Tate, Ivan; Bradley, Ryan; Howell, Thomas. On Road vs. Off Road Low Load Cycle Comparison. SAE Technical Paper, 2024. https://trid.trb.org/view/2367799

Please contact the NJDOT research librarian, Eric Schwarz, MLIS, at (609) 963-1898, or email library@dot.nj.gov for assistance in your transportation research, or to customize your searches in TRID and other databases.

NJDOT’s Research Librarian Recognized by the Special Libraries Association with 2024 Innovation Award for Work on the NJDOT Memorial Wall

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The Special Libraries Association (SLA) recently announced that its 2024 Innovation Award recipient was Eric Schwarz, NJDOT’s Research Librarian, for his archival research work on the New Jersey DOT Memorial Wall. The SLA Transportation Community Board unanimously approved the nomination and a plaque, sponsored by National Rural Transit Assistance Program (RTAP), was provided in acknowledgement of the achievement. News of the award winners was announced via the National Transportation Knowledge Network (NTKN) Blog. The award will be officially announced at the SLA Annual Conference in Rhode Island later this month.

NJDOT Research Librarian, Eric Schwarz, with SLATRAN 2024 Innovation Award. Photo: Glenn Catana/NJDOT.

The SLA’s award announcement notes the following:

  • In 2000, the NJDOT erected an Employee Memorial wall with a plaque for each of the 32 employees known to have died under these circumstances. Over the years, four names were added, including those of employees who gave their lives in 2007 and 2010. This brought the pre-2023 total of known names to 36.
  • In early 2023, NJDOT Research Librarian Eric Schwarz found the names of five additional men who had sacrificed their lives, in an employee newspaper called The Highway, published from 1942 to 1950. These names were added to the wall during the NJDOT’s 23rd Annual Remembrance Ceremony and 22nd Anniversary of 9/11, held on September 11, 2023.
  • Using the accounts from The Highway, supplemented by research using the New Jersey State Library’s newspaper databases and draft registration cards from the military records database (Fold3), Eric pieced together the stories of these five men, their deaths, and their lives. He presented stories of these men, and of the archival and digitization work, as the keynote speaker at the NJDOT 2023 Remembrance Ceremony.
  • Then-New Jersey Transportation Commissioner Diane Gutierrez-Scaccetti presented Eric Schwarz with a plaque for his research leading to the addition of five names on the memorial wall.
  • Based on this work, Eric presented a poster at the TRB Annual Meeting on Jan. 8, 2024, “Discoveries in the First Year of New Jersey DOT’s Digitization Project.”  He also presented the project to the Transportation Librarians Roundtable, Special Libraries Association Transportation Community Collection Showcase, and several other venues.
Eric presented “lessons learned” implementing Digitization Project during TRB poster session at Annual Meeting in Washington DC.

Earlier this year, Eric gave a “Lunch and Learn” presentation to NJDOT employees that provided information about NJDOT’s Digitization Project along with the poster presented at the 2024 TRB Annual Meeting,  

More information about the online resources and historical documents that have been compiled with support from about Transportation Research and Connectivity Pooled Fund Study Digitization Project (TPF-5(442)) study were shared during the presentation.

Resources

Did You Know? NJDOT’s Research Library Resources

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The New Jersey Department of Transportation (NJDOT) Research Library offers valuable assistance in supporting various research tasks and for accessing resources. This article highlights three key resources available:

AASHTO Standards. All of the current “featured” or “essential” standards, manuals, and guides from the American Association of State Highway and Transportation Officials (AASHTO) are available to NJDOT employees. Some are available in print and/or CD-ROM and can be checked out with your New Jersey State Library card. All of the “featured/essential” publications are available to NJDOT employees via SharePoint. However, users must request access to individual publications and follow a specific download process. Many additional standards, both current and historical, are also available.

ASTM Standards. Formed in 1898, ASTM International is one of the world’s largest international standards developing organizations. NJDOT subscribes to the ASTM Compass database, which includes 81,757 standards from ASTM and 2,282 publications from AASHTO, as of May 21, 2024. The Research Library will be happy to help you retrieve specific standards available under this subscription. NJDOT employees who access ASTM standards frequently may also sign up for their own logins under the “Tools” page on the intranet.

TRID Database. TRID is an integrated database that combines the records from TRB’s Transportation Research Information Services (TRIS) Database and the OECD’s Joint Transport Research Centre’s International Transport Research Documentation (ITRD) Database. TRID provides access to 1.4 million records of transportation research worldwide.

Hot Topic Searches are available on the TRID Searches page

The Research Library maintains a “TRID Searches” page that contains a list of recent publications indexed in the TRID database organized by 37 subject areas. NJDOT’s Library also maintains “Hot Topic” searches that contain the projects and publications issued in the last five years on several topics, including: Transformational Technologies; Planning & Safety; Resilience; Sustainability; Diversity, Equity and Inclusion; and Workforce Recruitment and Retention.

RECENT NJ PUBLICATIONS IN TRID

Recent publications with New Jersey identifiers and/or prepared by NJ research institutions can be discovered through TRID.  A quick search in TRID of research from New Jersey published in the past six months included these articles:

Artificial Intelligence (AI) and Safety

  • Zaman, A., Z. Huang, W. Li, H. Qin, D. Kang, and X. Liu. Development of Railroad Trespassing Database Using Artificial Intelligence. Rutgers University, New Brunswick, Federal Railroad Administration, Federal Railroad Administration, 2024, 80p. https://trid.trb.org/view/2341095

Bridges and Other Structures

  • Najafi, A., Z. Amir, B. Salman, P. Sanaei, E. Lojano-Quispe, A. Maher, and R. Schaefer. A Digital Twin Framework for Bridges. ASCE International Conference on Computing in Civil Engineering 2023, American Society of Civil Engineers, 2024, pp 433-441. https://trid.trb.org/view/2329319
  • Al Shaini, I., and A. Trias. Bridge deck surface damage assessment using point cloud data. Advances in Bridge Engineering, Vol. 4, No. 1, 2023, 31p. https://trid.trb.org/view/2301538

Environment and Underserved Communities

  • Ji, N., A. Baptista, C.H. Yu, C. Cepeda, F. Green, M. Greenberg, I. Colon Mincey, P. Ohman-Strickland, N. Fiedler, H.M. Kipen, and R.J. Laumbach. Traffic-related air pollution, chronic stress, and changes in exhaled nitric oxide and lung function among a panel of children with asthma living in an underresourced community. Science of the Total Environment, Vol. 912, 2024, p168984. https://trid.trb.org/view/2302836

Safety and Human Factors

  • Bartin, B., K. Ozbay, and C. Xu. Safety performance functions for two-lane urban arterial segments. Safety Science, Vol. 167, 2023, p106284. https://trid.trb.org/view/2229553
  • Hasan, A.S., D. Patel, and M. Jalayer. Did COVID-19 Mandates influence driver distraction behaviors? A case study in New Jersey. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 99, 2023, pp 429-449. https://trid.trb.org/view/2289812
  • Patel, D., R.E. Alfaris, and M. Jalayer. Assessing the effectiveness of autism spectrum disorder roadway warning signs: A case study in New Jersey. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 100, 2024, pp 57-68. https://trid.trb.org/view/2293015
  • Younes, H., R.B. Noland, and C.J. Andrews. Gender split and safety behavior of cyclists and e-scooter users in Asbury Park, NJ. Case Studies on Transport Policy, Vol. 14, 2023, p 101073. https://trid.trb.org/view/2238150
  • Younes, H., R.B. Noland, L.A. Von Hagen, and J. Sinclair. Cycling during and after COVID: Has there been a boom in activity? Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 99, 2023, pp 71-82. https://trid.trb.org/view/2274405

Public Participation

  • Keenan, K. The transportation policy elite and their ladder of citizen participation: Problems and prospects around communication methods in New Jersey. Cities, Vol. 145, 2024, p 104732. https://trid.trb.org/view/2309380

Public Transportation Ridership

  • Devajyoti, D. and Z. Liu. Who stopped riding buses and what would motivate them to return? A New Jersey case study. Case Studies on Transport Policy, Vol. 15, 2024, p 101159. https://trid.trb.org/view/2343481

NJDOT FUNDED RESEARCH

NJDOT’s Bureau of Research, Innovation & Information Transfer (BRIIT), which includes the Research Library, funds research to enhance the quality and cost effectiveness of the policies, practices, standards and specifications that are used in planning, designing, building and maintaining New Jersey’s transportation infrastructure. BRIIT collaborates directly with university and other research professionals to find solutions to improve the durability and efficiency of infrastructure and the safety and mobility of New Jersey’s residents, workers, visitors and businesses. Ongoing research projects and completed research studies can be accessed here.

NJDOT’s BRIIT prepares an Annual Implementation Report that explore the value and benefits of its funded research. These reports survey and interview principal investigators, customers and research project managers to help identify next steps for research and implementation and document the strategies that have been used for technology transfer of research findings to the state’s transportation community. The most recent report, published in February 2024, covers research completed in 2021-2022.

Please contact the NJDOT research librarian, Eric Schwarz, MLIS, at (609) 963-1898, or email library@dot.nj.gov for assistance in your transportation research, or to customize your searches in TRID and other databases.