This report assists public agencies in the inventorying, documenting, and configuring traffic management system (TMS) assets, and illustrates how such information can support the management of these assets. It provides information for agencies to establish and manage their TMS asset inventories. This document addresses key factors in determining wh
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MacAdam, J., Belella, P., Lukasik, D., & Sanchez, R. (2024). Inventorying, Documenting, and Configuring Traffic Management System (TMS) Assets and Resources (Report No. FHWA-HRT-25-031). United States. Federal Highway Administration. Office of Safety and Operations Research and Development. https://doi.org/10.21949/1521542
MacAdam, John, Paul Belella, Dan Lukasik, and Robert Sanchez. Inventorying, Documenting, and Configuring Traffic Management System (TMS) Assets and Resources. Report no. FHWA-HRT-25-031. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024. https://doi.org/10.21949/1521542.
MacAdam, John, et al. Inventorying, Documenting, and Configuring Traffic Management System (TMS) Assets and Resources. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024, Report no. FHWA-HRT-25-031, ROSA P. https://doi.org/10.21949/1521542.
This report examines emerging data sources applicable to traffic simulation model calibration. The project team categorized these data sources based on publicly accessible datasets and emphasized their potential use for enhancing traffic simulation model calibration. The study evaluates the strengths and limitations of these emerging data sources a
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Zhou, X. (., Luo, X. (., Abbasi, M., Huang, Z., & Tyagi, A. (2024). Emerging Data Cleaning and Fusion for Traffic Model Calibration–Data Fusion for Microsimulation Model Calibration (Report No. FHWA-HRT-24-142). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521587
Zhou, Xuesong (Simon), Xiangyong (Roy) Luo, Mohammad Abbasi, Zhitong Huang, and Ankur Tyagi. Emerging Data Cleaning and Fusion for Traffic Model Calibration–Data Fusion for Microsimulation Model Calibration. Report no. FHWA-HRT-24-142. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521587.
Zhou, Xuesong (Simon), et al. Emerging Data Cleaning and Fusion for Traffic Model Calibration–Data Fusion for Microsimulation Model Calibration. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-24-142, ROSA P. https://doi.org/10.21949/1521587.
United States. Department of Transportation. Federal Highway Administration. Office of Operations
2024-12-01
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Urban Congestion Report (UCR)
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The Urban Congestion Report (UCR) is produced on a quarterly basis and characterizes the most recent traffic congestion and reliability trends at the national and city level. Each quarterly UCR compares data from the most recent three months to the same three months in the previous year.
United States. Department of Transportation. Federal Highway Administration. Office of Operations (2024). Urban Congestion Report (UCR): Year-to-Year Trends in the U.S. for October through December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations. https://rosap.ntl.bts.gov/view/dot/87763
United States. Department of Transportation. Federal Highway Administration. Office of Operations. Urban Congestion Report (UCR): Year-to-Year Trends in the U.S. for October through December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations, 2024. https://rosap.ntl.bts.gov/view/dot/87763.
United States. Department of Transportation. Federal Highway Administration. Office of Operations Urban Congestion Report (UCR): Year-to-Year Trends in the U.S. for October through December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/87763.
Driver assistance systems that facilitate economical driving (eco-driving) aim to reduce greenhouse gas emissions and improve vehicle efficiency. These systems reduce idling time at intersections and smooth acceleration and deceleration patterns. Eco-driving has the potential to improve driving comfort by smoothing speed profiles. This study explor
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Sanchez, R., Ahmed, A., Chao, S. F., Weaver, S., & Eisert, J. (2024). Exploring the Effects of Vehicle Automation and Cooperative Messaging on Mixed Fleet Eco-Drive Interactions (Report No. FHWA-HRT-24-169). United States. Federal Highway Administration. Office of Safety and Operations Research and Development. https://doi.org/10.21949/1521613
Sanchez, Robert, Ananna Ahmed, Szu-Fu Chao, Starla Weaver, and Jesse Eisert. Exploring the Effects of Vehicle Automation and Cooperative Messaging on Mixed Fleet Eco-Drive Interactions. Report no. FHWA-HRT-24-169. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024. https://doi.org/10.21949/1521613.
Sanchez, Robert, et al. Exploring the Effects of Vehicle Automation and Cooperative Messaging on Mixed Fleet Eco-Drive Interactions. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024, Report no. FHWA-HRT-24-169, ROSA P. https://doi.org/10.21949/1521613.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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This document provides a summary of Exploratory Advanced Research (EAR) funded research projects; it is a compendium of papers covering the following topics: Mobile Ad Hoc Networks, Digital Twins, Supplementary Materials, Artificial Intelligence (AI), Blockchain, Waste Plastics in Asphalt Binders, Interagency Research.
United States. Department of Transportation. Federal Highway Administration (2024). Exploratory Advanced Research (EAR) Program Compendium of Papers from Funded Research Projects - Updated for 2024 (Report No. FHWA-HRT-25-018). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521535
United States. Department of Transportation. Federal Highway Administration. Exploratory Advanced Research (EAR) Program Compendium of Papers from Funded Research Projects - Updated for 2024. Report no. FHWA-HRT-25-018. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521535.
United States. Department of Transportation. Federal Highway Administration Exploratory Advanced Research (EAR) Program Compendium of Papers from Funded Research Projects - Updated for 2024. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-018, ROSA P. https://doi.org/10.21949/1521535.
The Federal Highway Administration's Long-Term Pavement Performance (LTPP) program issues a triannual newsletter to inform you about the progress and activities of the program. Brief updates on progress in important program activities are provided, including data collection, data releases, new products and publications, pooled fund studies, and dat
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Long-Term Pavement Performance Program (U.S.) (2024). LTPP Newsletter - December 2024 - Fall Issue (Report No. FHWA-HRT-25-048). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521550
Long-Term Pavement Performance Program (U.S.). LTPP Newsletter - December 2024 - Fall Issue. Report no. FHWA-HRT-25-048. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521550.
Long-Term Pavement Performance Program (U.S.) LTPP Newsletter - December 2024 - Fall Issue. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-048, ROSA P. https://doi.org/10.21949/1521550.
During a full-scale fatigue test program at Lehigh University performed in 2018, the fatigue performance of a prototype orthotropic steel deck (OSD) with extended cut-out (EC) rib-to-floor beam (RFB) connections with partial joint penetration (PJP) welds and reinforcing fillet welds which wrap-around at the weld termination was investigated. The fu
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Hodgson, I., Sause, R., & Kozy, B. M. (2024). Improving the Manufacturability of Extended Cut-Out Rib-to-Floor Beam Connections for Orthotropic Steel Decks (Report No. 24-01). Lehigh University. https://rosap.ntl.bts.gov/view/dot/79025
Hodgson, Ian, Richard Sause, and Brian M. Kozy. Improving the Manufacturability of Extended Cut-Out Rib-to-Floor Beam Connections for Orthotropic Steel Decks. Report no. 24-01. Lehigh University, 2024. https://rosap.ntl.bts.gov/view/dot/79025.
Hodgson, Ian, et al. Improving the Manufacturability of Extended Cut-Out Rib-to-Floor Beam Connections for Orthotropic Steel Decks. Lehigh University, 2024, Report no. 24-01, ROSA P. https://rosap.ntl.bts.gov/view/dot/79025.
United States. Department of Transportation. Federal Highway Administration. Office of Operations
2024-12-01
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National Travel Time Dashboard: Interstate Highways
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The Monthly Urban Congestion Report (UCR) provides a state-level overview of traffic congestion and reliability trends on Interstate highways. Published every month, the report compares data from the most recent month with the same month in the previous year. It uses travel time data from the Federal Highway Administration’s National Performance Ma
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United States. Department of Transportation. Federal Highway Administration. Office of Operations (2024). National Travel Time Dashboard: Interstate Highways, December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations. https://rosap.ntl.bts.gov/view/dot/87941
United States. Department of Transportation. Federal Highway Administration. Office of Operations. National Travel Time Dashboard: Interstate Highways, December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations, 2024. https://rosap.ntl.bts.gov/view/dot/87941.
United States. Department of Transportation. Federal Highway Administration. Office of Operations National Travel Time Dashboard: Interstate Highways, December 2024. United States. Department of Transportation. Federal Highway Administration. Office of Operations, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/87941.
Extracting information from naturalistic driving datasets is crucial to understanding driver behavior and distractions. To overcome some of the challenges involved with annotating the data, detecting objects and behaviors, and storing large datasets, the researchers set out to develop a robust platform that can automatically estimate driver state,
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Sharma, A., Sarkar, S., Hegde, C., Ozcan, K., Velipasalar, S., Rizzo, M., Merickel, J., & Adu-Gyamfi, Y. (2024). Deep InSight: A Driver-State Estimation Platform for Processing Naturalistic Driving Data (Report No. FHWA-HRT-24-137). United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. https://doi.org/10.21949/1521583
Sharma, Anuj, Soumik Sarkar, Chinmay Hegde, Koray Ozcan, Senem Velipasalar, Matthew Rizzo, Jennifer Merickel, and Yaw Adu-Gyamfi. Deep InSight: A Driver-State Estimation Platform for Processing Naturalistic Driving Data. Report no. FHWA-HRT-24-137. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024. https://doi.org/10.21949/1521583.
Sharma, Anuj, et al. Deep InSight: A Driver-State Estimation Platform for Processing Naturalistic Driving Data. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024, Report no. FHWA-HRT-24-137, ROSA P. https://doi.org/10.21949/1521583.
Short-span bridge superstructures are commonly made of precast concrete elements, and those elements are proportioned according to the properties of conventional concrete. The emergence of ultra-high-performance concrete (UHPC), and the release of the AASHTO Guide Specification for Structural Design with UHPC, creates new possibilities for the rede
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Garber, D., El-Helou, R., & Graybeal, B. A. (2024). Section Shapes for Short-Span UHPC Bridges (Report No. FHWA-RC-24-0009). United States. Federal Highway Administration. Office of Technical Services. https://rosap.ntl.bts.gov/view/dot/82189
Garber, David, Rafic El-Helou, and Benjamin A. Graybeal. Section Shapes for Short-Span UHPC Bridges. Report no. FHWA-RC-24-0009. United States. Federal Highway Administration. Office of Technical Services, 2024. https://rosap.ntl.bts.gov/view/dot/82189.
Garber, David, et al. Section Shapes for Short-Span UHPC Bridges. United States. Federal Highway Administration. Office of Technical Services, 2024, Report no. FHWA-RC-24-0009, ROSA P. https://rosap.ntl.bts.gov/view/dot/82189.
This TechNote covers the research conducted regarding how key features such as vertical alignment is inspected and measured during construction and the challenges and solutions performed properly so that improper placement or damage on the retaining walls - mechanically stabilized earth (MSE) can be detected in a timely manner.
Che, E., Thorsen, M., & Roe, G. (2024). Pocket Lidar for Assessing Mechanically Stabilized Earth (MSE) Retaining Wall for Bridge Abutments [TechNote] (Report No. FHWA-HRT-25-013). United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. https://doi.org/10.21949/1521529
Che, Ezra, Mitch Thorsen, and Gene Roe. Pocket Lidar for Assessing Mechanically Stabilized Earth (MSE) Retaining Wall for Bridge Abutments [TechNote]. Report no. FHWA-HRT-25-013. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024. https://doi.org/10.21949/1521529.
Che, Ezra, et al. Pocket Lidar for Assessing Mechanically Stabilized Earth (MSE) Retaining Wall for Bridge Abutments [TechNote]. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024, Report no. FHWA-HRT-25-013, ROSA P. https://doi.org/10.21949/1521529.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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This report, The U.S. Department of Transportation (USDOT) has prioritized developing technologies that promote traffic safety and improve mobility for all travelers. Supporting that USDOT priority, the Exploratory Advanced Research (EAR) Program of the Federal Highway Administration (FHWA) explores the development of Artificial Intelligence (AI) a
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United States. Department of Transportation. Federal Highway Administration (2024). The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation - 2024 Updates (Report No. FHWA-HRT-25-020). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521537
United States. Department of Transportation. Federal Highway Administration. The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation - 2024 Updates. Report no. FHWA-HRT-25-020. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521537.
United States. Department of Transportation. Federal Highway Administration The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation - 2024 Updates. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-020, ROSA P. https://doi.org/10.21949/1521537.
Naturalistic driving data provides a wealth of information for researchers studying driver behavior and distracted driving. However, manually annotating the videos to extract the data is costly and time consuming. This project’s research team set out to develop a system to analyze videos from the second Strategic Highway Research Program Naturalist
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Sarkar, A., Abbott, L., Hickman, J. S., Papakis, I., Winkowski, C., Datta, D., Sonth, A., Bhagat, H., Bhat, S., Jain, S., Svetovidov, A., Hanowski, R., & Kaskar, O. (2024). Using Video Analytics to Automatically Annotate Driver Behavior and Context in Naturalistic Driving Data (Report No. FHWA-HRT-24-078). United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. https://doi.org/10.21949/1521750
Sarkar, Abhijit, Lynn Abbott, Jeffrey S. Hickman, Ioannis Papakis, Calvin Winkowski, Debanjan Datta, and Akash Sonth, et al.. Using Video Analytics to Automatically Annotate Driver Behavior and Context in Naturalistic Driving Data. Report no. FHWA-HRT-24-078. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024. https://doi.org/10.21949/1521750.
Sarkar, Abhijit, et al. Using Video Analytics to Automatically Annotate Driver Behavior and Context in Naturalistic Driving Data. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024, Report no. FHWA-HRT-24-078, ROSA P. https://doi.org/10.21949/1521750.
The reconstruction of the Federal Highway Administration’s (FHWA) third-generation Pavement Testing Facility (PTF-3) provided a unique opportunity to incorporate the geotechnical aspects of pavements within the agency’s accelerated pavement testing program. These geotechnical aspects included various unbound and asphalt-treated base materials to es
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Adams, M. T., Nicks, J., Zuniga, I. E., Ghaaowd, I. I., & Culbreth, N. (2024). Selection and Characterization of the Foundation Materials for the Third-Generation FHWA Pavement Test Facility (Report No. FHWA-HRT-25-023). United States. Department of Transportation. Federal Highway Administration. Office of Infrastructure Research and Development. https://doi.org/10.21949/1521622
Adams, Michael T., Jennifer Nicks, Isaac E Zuniga, Ismaail I. Ghaaowd, and Nicholas Culbreth. Selection and Characterization of the Foundation Materials for the Third-Generation FHWA Pavement Test Facility. Report no. FHWA-HRT-25-023. United States. Department of Transportation. Federal Highway Administration. Office of Infrastructure Research and Development, 2024. https://doi.org/10.21949/1521622.
Adams, Michael T., et al. Selection and Characterization of the Foundation Materials for the Third-Generation FHWA Pavement Test Facility. United States. Department of Transportation. Federal Highway Administration. Office of Infrastructure Research and Development, 2024, Report no. FHWA-HRT-25-023, ROSA P. https://doi.org/10.21949/1521622.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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A product of the Federal Highway Administration's (FHWAs) Safety Research and Development Program, the Interactive Highway Safety Design Model (IHSDM) is a suite of safety analysis tools to evaluate the safety and operational effects of geometric-design decisions on two-lane rural highways. The following resources are available to individuals consi
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United States. Department of Transportation. Federal Highway Administration (2024). IHSDM Resource List [December 2024] (Report No. FHWA-HRT-24-180). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521518
United States. Department of Transportation. Federal Highway Administration. IHSDM Resource List [December 2024]. Report no. FHWA-HRT-24-180. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521518.
United States. Department of Transportation. Federal Highway Administration IHSDM Resource List [December 2024]. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-24-180, ROSA P. https://doi.org/10.21949/1521518.
The use of nondestructive evaluation (NDE) for the condition assessment of bridge decks is growing and has the potential to reduce operating costs and extend lifecycles by fostering cost-effective and timely interventions. Highway agencies recognize that preservation treatments can impact the lifecycle costs of bridges. Several NDE technologies ide
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Brown, M. C., ElBatanouny, M. K., Bektas, B., Azari, H., Foden, A., Imani, A., & Hawkins, K. (2024). Incorporating Nondestructive Evaluation Methods into Bridge Deck Preservation Strategies (Report No. FHWA-HRT-25-009). United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. https://doi.org/10.21949/1521524
Brown, Michael C, Mohamed K. ElBatanouny, Basak Bektas, Hoda Azari, Andrew Foden, Arezoo Imani, and Katherine Hawkins. Incorporating Nondestructive Evaluation Methods into Bridge Deck Preservation Strategies. Report no. FHWA-HRT-25-009. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024. https://doi.org/10.21949/1521524.
Brown, Michael C, et al. Incorporating Nondestructive Evaluation Methods into Bridge Deck Preservation Strategies. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024, Report no. FHWA-HRT-25-009, ROSA P. https://doi.org/10.21949/1521524.
This technote summarizes the research conducted to explore the capabilities and limitations of pocket lidar for curb ramp inspections. Specifically, the study focuses on the ability of the pocket lidar to extract slope measurements, which often poses difficulties in reliably obtaining this information.
Olsen, M., Che, E., Caya, J., Caya, M., Roe, G., Schneider, J., & Embacher, R. (2024). Pocket Lidar Curb Ramp Assessments [TechNote] (Report No. FHWA-HRT-25-014). United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. https://doi.org/10.21949/1521530
Olsen, Michael, Ezra Che, John Caya, Michael Caya, Gene Roe, James Schneider, and Rebecca Embacher. Pocket Lidar Curb Ramp Assessments [TechNote]. Report no. FHWA-HRT-25-014. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024. https://doi.org/10.21949/1521530.
Olsen, Michael, et al. Pocket Lidar Curb Ramp Assessments [TechNote]. United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology, 2024, Report no. FHWA-HRT-25-014, ROSA P. https://doi.org/10.21949/1521530.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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This flyer provides a summary of FHWA-HRT-25-035 and CR code to access the full report.
United States. Department of Transportation. Federal Highway Administration (2024). Exploring Risk Factors Contributing to Disparities in Pedestrian and Bicyclist Fatalities and Serious Injuries (Report No. FHWA-HRT-25-043). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521425
United States. Department of Transportation. Federal Highway Administration. Exploring Risk Factors Contributing to Disparities in Pedestrian and Bicyclist Fatalities and Serious Injuries. Report no. FHWA-HRT-25-043. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521425.
United States. Department of Transportation. Federal Highway Administration Exploring Risk Factors Contributing to Disparities in Pedestrian and Bicyclist Fatalities and Serious Injuries. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-043, ROSA P. https://doi.org/10.21949/1521425.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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This postcard is to announce the Excellence in Highway Safety Data Awards Student Contest 2025. The intended audience is all undergraduate, graduate, and community college students with a passion for impactful research and data analysis.
United States. Department of Transportation. Federal Highway Administration (2024). Excellence in Highway Safety Data Awards [Postcard] (Report No. FHWA-HRT-25-024). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521538
United States. Department of Transportation. Federal Highway Administration. Excellence in Highway Safety Data Awards [Postcard]. Report no. FHWA-HRT-25-024. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521538.
United States. Department of Transportation. Federal Highway Administration Excellence in Highway Safety Data Awards [Postcard]. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-024, ROSA P. https://doi.org/10.21949/1521538.
United States. Department of Transportation. Federal Highway Administration
2024-12-01
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This flyer promotes the Federal Highway Administration’s (FHWA's) Exploratory Advanced Research (EAR) Program booth that will be at the 2025 Transportation Research Board Annual Meeting.
United States. Department of Transportation. Federal Highway Administration (2024). Exploratory Advanced Research (EAR) Program Highlights (Report No. FHWA-HRT-25-040). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/1521422
United States. Department of Transportation. Federal Highway Administration. Exploratory Advanced Research (EAR) Program Highlights. Report no. FHWA-HRT-25-040. United States. Department of Transportation. Federal Highway Administration, 2024. https://doi.org/10.21949/1521422.
United States. Department of Transportation. Federal Highway Administration Exploratory Advanced Research (EAR) Program Highlights. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-25-040, ROSA P. https://doi.org/10.21949/1521422.
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