By leveraging advanced technologies, Autonomous Vehicles (AVs) hold the potential to increase transportation safety and efficiency. This collection showcases USDOT-funded research and data concerning AVs. Bookmark this collection: https://rosap.ntl.bts.gov/collection_avs OR https://doi.org/10.21949/1x81-qs91.
Taking advantage of the rapid development of vehicle control and communication technologies, many studies have suggested that operating vehicles in platoons in the future would help improve the safety and efficiency of the transportation system. Although vehicles in a platoon can share data from V2/V communication, a platoon model built on proper c
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Yi, P., & Alqubaysi, T. (2023). Impact Analysis of Roadway Surface and Vehicle Conditions on Fleet Formation for Connected and Automated Vehicles (Report No. UA-CETran-2023-03). Center for Connected and Automated Transportation. Purdue University. https://rosap.ntl.bts.gov/view/dot/73989
Yi, Ping and Tariq Alqubaysi. Impact Analysis of Roadway Surface and Vehicle Conditions on Fleet Formation for Connected and Automated Vehicles. Report no. UA-CETran-2023-03. Center for Connected and Automated Transportation. Purdue University, 2023. https://rosap.ntl.bts.gov/view/dot/73989.
Yi, Ping, and Tariq Alqubaysi Impact Analysis of Roadway Surface and Vehicle Conditions on Fleet Formation for Connected and Automated Vehicles. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. UA-CETran-2023-03, ROSA P. https://rosap.ntl.bts.gov/view/dot/73989.
This study introduces a unified end-to-end framework for analyzing network traffic equilibrium. The framework learns supply and demand components directly from traffic data, using computational graphs to parameterize unknown elements. It enforces user equilibrium through variational inequalities and can incorporate various modeling approaches, incl
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Yin, Y., & Liu, Z. (2023). AI-Enabled Transportation Network Analysis, Planning and Operations (Report No. CCAT Report No. 65). University of Michigan. Center for Connected and Automated Transportation. https://hdl.handle.net/2027.42/177545
Yin, Yafeng and Zhichen Liu. AI-Enabled Transportation Network Analysis, Planning and Operations. Report no. CCAT Report No. 65. University of Michigan. Center for Connected and Automated Transportation, 2023. https://hdl.handle.net/2027.42/177545.
Yin, Yafeng, and Zhichen Liu AI-Enabled Transportation Network Analysis, Planning and Operations. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. CCAT Report No. 65, ROSA P. https://hdl.handle.net/2027.42/177545.
The number of automated features in surface vehicles is increasing as new vehicles are released each year. Some features allow drivers to temporarily take their attention off the road to engage in other tasks. However, sometimes it is important for drivers to immediately take control of the vehicle. To take control safely, drivers must understand w
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Dataset
Greatbatch, R., Dunn, N. J., Kim, H., Krasner, A., Doerzaph, Z., & Llaneras, R. E. (2023). Guiding Driver Responses During Manual Takeovers from Automated Vehicles [supporting dataset] (Report No. VTTI-00-026). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/FJYGL1
Greatbatch, Richard, Naomi J. Dunn, Hyungil Kim, Alexander Krasner, Zachary Doerzaph, and Robert E. Llaneras. Guiding Driver Responses During Manual Takeovers from Automated Vehicles [supporting dataset]. Report no. VTTI-00-026. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://doi.org/10.15787/VTT1/FJYGL1.
Greatbatch, Richard, et al. Guiding Driver Responses During Manual Takeovers from Automated Vehicles [supporting dataset]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. VTTI-00-026, ROSA P. https://doi.org/10.15787/VTT1/FJYGL1.
Truck platooning—wirelessly linking two or more trucks to travel in a closely spaced convoy—is federally promoted to save fuel, improve the environment, and improve traffic operations. Platooning places trucks much closer than current design codes anticipate. While this strategy can provide higher fuel efficiency, it also can potentially overload s
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Steelman, J. S., Puckett, J. A., Linzell, D. G., & Yang, B. (2023). Truck Platooning Effects on Girder Bridges: Phase II-Service (Report No. WBS 26-1107-0202-001). Nebraska. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/68881
Steelman, Joshua S., Jay A. Puckett, Daniel G. Linzell, and Bowen Yang. Truck Platooning Effects on Girder Bridges: Phase II-Service. Report no. WBS 26-1107-0202-001. Nebraska. Department of Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/68881.
Steelman, Joshua S., et al. Truck Platooning Effects on Girder Bridges: Phase II-Service. Nebraska. Department of Transportation, 2023, Report no. WBS 26-1107-0202-001, ROSA P. https://rosap.ntl.bts.gov/view/dot/68881.
We will apply our results from neuroscience and safe control to improve driver-assistance technology as follows. First, we will study the use of visual attention information to detect risks early, before the failure to detect risk-critical obstacles can be identified from the drivers' control action and vehicle states. Second, we will find safer co
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Dataset
Nakahira, Y. (2023). Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems [supporting datasets]. Mobility21 (UTC). https://doi.org/10.21949/1529858
Nakahira, Yorie. Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems [supporting datasets]. Mobility21 (UTC), 2023. https://doi.org/10.21949/1529858.
Nakahira, Yorie Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems [supporting datasets]. Mobility21 (UTC), 2023, ROSA P. https://doi.org/10.21949/1529858.
This paper focuses on assessing the transportation system and sub-population level impacts of different congestion pricing policies for shared AV services in Seattle. While the conclusions of this research are meant to be generalizable, we focus our study on Seattle, Washington because it’s a diverse city with known inequalities among income, race,
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Harper, C., & Yang, H. (2023). Insights into Equitable and Fair Congestion Pricing Strategies in a World of Shared Automated Vehicles (Report No. 69A3551747111). Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68486
Harper, Corey and Haoming Yang. Insights into Equitable and Fair Congestion Pricing Strategies in a World of Shared Automated Vehicles. Report no. 69A3551747111. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68486.
Harper, Corey, and Haoming Yang Insights into Equitable and Fair Congestion Pricing Strategies in a World of Shared Automated Vehicles. Mobility21, Carnegie Mellon University, 2023, Report no. 69A3551747111, ROSA P. https://rosap.ntl.bts.gov/view/dot/68486.
Roadway intersections are among the major causes of traffic congestion besides lane reduction bottlenecks. When a major road intersects a minor road at an unsignalized intersection without the control of a traffic signal, the mainline vehicles are given priority over the minor road vehicles to go through the intersection, and the latter can only en
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Yi, P. (2023). Access Control at Major/Minor Road Intersection Through CAV in Mixed Traffic (Report No. UA-CETran 2023-01). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.5703/1288284317738
Yi, Ping. Access Control at Major/Minor Road Intersection Through CAV in Mixed Traffic. Report no. UA-CETran 2023-01. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.5703/1288284317738.
Yi, Ping Access Control at Major/Minor Road Intersection Through CAV in Mixed Traffic. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UA-CETran 2023-01, ROSA P. https://doi.org/10.5703/1288284317738.
High precision road maps are a crucial component to facilitating autonomous driving techniques. Autonomous vehicles (AVs) are experiencing exponential growth. According to the latest forecast from IHS Markit, over 33 million AVs will be on the road globally by 2040, posing a higher requirement to ensure AVs’ driving safety. Although current AVs rel
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Steele, J., Wu, J., Duong, A., Wyatt, B., Tapp, C., Tambunga, J., Kuhr, J., Ma, J., & McCarty, M. (2023). Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV) [Summary]. Texas Department of Transportation. Research and Technology Implementation Office. https://rosap.ntl.bts.gov/view/dot/72738
Steele, Joanne, Jason Wu, Ash Duong, Bethany Wyatt, Charles Tapp, Jacob Tambunga, James Kuhr, Jianming Ma, and Matthew McCarty. Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV) [Summary]. Texas Department of Transportation. Research and Technology Implementation Office, 2023. https://rosap.ntl.bts.gov/view/dot/72738.
Steele, Joanne, et al. Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV) [Summary]. Texas Department of Transportation. Research and Technology Implementation Office, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72738.
Electric vehicles, autonomous or manual, provide a valuable opportunity to address issues of environmental pollution, climate change, and national security. In recognition of the synergies between vehicle electrification and autonomy, this study addresses the facilitation of vehicle electrification in the prospective future era where autonomous veh
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Pourgholamali, M., Davatgari, A., Wang, J., Benny, D., Miralinaghi, M., & Labi, S. (2023). Facilitating Electric-Propulsion of Autonomous Vehicles Through Efficient Design of a Charging-Facility Network (Report No. 40). University of Michigan. Center for Connected and Automated Transportation. http://dx.doi.org/10.5703/1288284317703
Pourgholamali, Mohammadhosein, Amir Davatgari, Jiaming Wang, Deepak Benny, Mohammad Miralinaghi, and Samuel Labi. Facilitating Electric-Propulsion of Autonomous Vehicles Through Efficient Design of a Charging-Facility Network. Report no. 40. University of Michigan. Center for Connected and Automated Transportation, 2023. http://dx.doi.org/10.5703/1288284317703.
Pourgholamali, Mohammadhosein, et al. Facilitating Electric-Propulsion of Autonomous Vehicles Through Efficient Design of a Charging-Facility Network. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. 40, ROSA P. http://dx.doi.org/10.5703/1288284317703.
A significant majority of state-of-the-art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment, making them more suited for urban communities. Conversely, many rural roads in the U.S. do not have lane markings and have irregular boundaries. These challenges are common to many small and ru
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Ninan, S., & Rathinam, S. (2023). Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas (Report No. 06-004). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/70413
Ninan, Stephen and Sivakumar Rathinam. Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas. Report no. 06-004. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/70413.
Ninan, Stephen, and Sivakumar Rathinam Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 06-004, ROSA P. https://rosap.ntl.bts.gov/view/dot/70413.
This report lays the theoretical groundwork for participatory traffic control that integrates traditional infrastructure like traffic signals with connected and automated vehicles (CAVs) acting as mobile actuators. The study is divided into two main parts. First, we introduce a robust traffic state estimation method that leverages real-time data fr
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Supporting Files
Wu, M., Wang, X., Yin, Y., Liu, H., Wang, B., & Lynch, J. P. (2023). Leveraging Connected and Automated Vehicles for Participatory Traffic Control (Report No. CCAT Report No. 56). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/8088
Wu, Minghui, Xingmin Wang, Yafeng Yin, Henry Liu, Ben Wang, and Jerome P Lynch. Leveraging Connected and Automated Vehicles for Participatory Traffic Control. Report no. CCAT Report No. 56. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/8088.
Wu, Minghui, et al. Leveraging Connected and Automated Vehicles for Participatory Traffic Control. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. CCAT Report No. 56, ROSA P. https://doi.org/10.7302/8088.
With the arrival of new technologies like connected and self-driving autonomous vehicles (AVs), the workload of regional dispatchers will increase. To this end, the CADS (Congestion Alerting Decision Support) tool was developed to support strategic transportation planning (on the order of weeks to months) and tactical transportation planning (on th
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Cummings, M., Rouphail, N. M., Srinivasan, R., Samandar, S., Das, T., Saleem, T., & Shah, N. (2023). Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation (Report No. FHWA/NC/TCE2020-02 (P2)). North Carolina Department of Transportation. Research and Development Unit. https://rosap.ntl.bts.gov/view/dot/78872
Cummings, M, Nagui M. Rouphail, R Srinivasan, S Samandar, T Das, T Saleem, and NR Shah. Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation. Report no. FHWA/NC/TCE2020-02 (P2). North Carolina Department of Transportation. Research and Development Unit, 2023. https://rosap.ntl.bts.gov/view/dot/78872.
Cummings, M, et al. Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation. North Carolina Department of Transportation. Research and Development Unit, 2023, Report no. FHWA/NC/TCE2020-02 (P2), ROSA P. https://rosap.ntl.bts.gov/view/dot/78872.
The research team aims to investigate how networked autonomous mobility, such as self-driving taxis or delivery robots, will reshape our understanding of privacy and explore technical tools for privacy-preserving operation on the individual level and group level. The team will conduct comprehensive and realistic analysis using public datasets colle
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Zhao, D., & Wu, Z. S. (2023). Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68539
Zhao, Ding and Zhiwei Steven Wu. Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68539.
Zhao, Ding, and Zhiwei Steven Wu Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68539.
There are numerous applications of the Autonomous Maintenance Technology (AMT) that have yet to be fully utilized or widely shared. There are many slow-moving operations conducted by Departments of Transportation (DOTs) with an attenuator (e.g., work zone set up, operations, take down, repairs, mowing, sweeping), platooning of two or more vehicles
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Miller, E., & Young, C. (2023). Literature Review Synthesizing the Current and Potential ATMA Applications (Report No. CDOT-2023-14). Colorado Department of Transportation. Applied Research & Innovations Branch. https://rosap.ntl.bts.gov/view/dot/80139
Miller, Erika and Chandler Young. Literature Review Synthesizing the Current and Potential ATMA Applications. Report no. CDOT-2023-14. Colorado Department of Transportation. Applied Research & Innovations Branch, 2023. https://rosap.ntl.bts.gov/view/dot/80139.
Miller, Erika, and Chandler Young Literature Review Synthesizing the Current and Potential ATMA Applications. Colorado Department of Transportation. Applied Research & Innovations Branch, 2023, Report no. CDOT-2023-14, ROSA P. https://rosap.ntl.bts.gov/view/dot/80139.
The authors will apply their results from neuroscience and safe control to improve driver-assistance technology as follows. First, the authors will study the use of visual attention information to detect risks early, before the failure to detect risk-critical obstacles can be identified from the drivers' control action and vehicle states. Second, t
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Jing, H., Wang, Z., & Nakahira, Y. (2023). Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68701
Jing, Haoming, Zhuoyuan Wang, and Yorie Nakahira. Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68701.
Jing, Haoming, et al. Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68701.
According to studies done by Pew Research, PAVE, and AAA [2], [5], [3], people are quite apprehensive about being passengers in self-driving vehicles. They are concerned about the ethical choices that AV systems might make and how AV manufacturers prioritize pedestrians, passengers, or other drivers in an unavoidable accident. Drivers worry whether
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Mangharam, R., Loeb, H., & Qiao, Z. (2023). Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/70407
Mangharam, Rahul, Helen Loeb, and Zhijie Qiao. Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/70407.
Mangharam, Rahul, et al. Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/70407.
The aim of Project 0-7129 was to demonstrate that AV trucking data can be effectively ingested and reported to TxDOT maintenance personnel, improving operational efficiency. Texas leads the nation in AV testing and deployment, with significant stakeholder support for the IRMF’s potential to reduce reporting latency and improve geographical and seve
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Chin, K., Gold, A., McAuley, A., Werner, M., & Zhang, Z. (2023). Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief] (Report No. 0-7129). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/77787
Chin, Kristie, Andrea Gold, Anna McAuley, Mark Werner, and Zhanmin Zhang. Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief]. Report no. 0-7129. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/77787.
Chin, Kristie, et al. Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief]. University of Texas at Austin. Center for Transportation Research, 2023, Report no. 0-7129, ROSA P. https://rosap.ntl.bts.gov/view/dot/77787.
This project will explore several potential applications of image processing, including NN/deep learning technologies, to the analysis of traffic scenes involving passenger and transit vehicles. The project team outlines three potential applications below- the exact distribution of effort and topics addressed will depend on the availability of stud
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Yang, D., Ozguner, U., Redmill, K., Yurtsever, E., Capito, L., & Zhang, H. (2023). Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC). https://rosap.ntl.bts.gov/view/dot/68477
Yang, Dongfang, Umit Ozguner, Keith Redmill, Ekim Yurtsever, Linda Capito, and Haolin Zhang. Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/68477.
Yang, Dongfang, et al. Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68477.
Vehicle-pedestrian interactions in shared spaces represents a complex safety problem. Ideally, the vehicle must react safely to any pedestrian behavior, while the pedestrian behavior itself can be very complex and unpredictable. To emphasize this safety problem, in a 2019 Traffic Safety Facts report by the National Highway Traffic Safety Administra
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Hejase, B., Saim, M., Yang, D., Ko, M., Johora, F., Redmill, K., & Ozguner, U. (2023). Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68737
Hejase, Bilal, Muhammad Saim, Dongfang Yang, Mert Ko, FatemaT Johora, Keith Redmill, and Umit Ozguner. Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68737.
Hejase, Bilal, et al. Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68737.
As the population of older drivers continues to grow, there is an increasing need to enhance their awareness and understanding of Connected and Automated Vehicles (CAVs). This study utilized an education program aimed at improving the knowledge and awareness of older drivers about CAVs, thereby preparing them to utilize the safety features of these
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Yi, P., Marovic, C., & Whittenberger, R. (2023). Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies (Report No. UA-CETran 2023-04). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/73844
Yi, Ping, Claudia Marovic, and Reneé Whittenberger. Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies. Report no. UA-CETran 2023-04. University of Michigan. Center for Connected and Automated Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/73844.
Yi, Ping, et al. Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UA-CETran 2023-04, ROSA P. https://rosap.ntl.bts.gov/view/dot/73844.
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