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.
Research on firm location choice has traditionally received less attention compared to residential location choices. This study focuses on modeling the location choice of smaller economic units (establishments) within the framework of the North American Industrial Classification System (NAICS) sectors. It seeks to uncover critical insights into the
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Mishra, S., Golias, M., & Samani, A. R. (2022). Incorporating Freight in Regional Land Use Planning Models. Freight Mobility Research Institute. Florida Atlantic University. https://rosap.ntl.bts.gov/view/dot/76991
Mishra, Sabya, Mihalis Golias, and Ali Riahi Samani. Incorporating Freight in Regional Land Use Planning Models. Freight Mobility Research Institute. Florida Atlantic University, 2022. https://rosap.ntl.bts.gov/view/dot/76991.
Mishra, Sabya, et al. Incorporating Freight in Regional Land Use Planning Models. Freight Mobility Research Institute. Florida Atlantic University, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/76991.
Automation is the future of transportation. Research on autonomous driving technology and vehicles is taking place throughout America, and the technology is primed to transform existing and future transportation systems. As the technology for autonomous vehicles continues to develop and eventually becomes ready for real-world testing, cooperative a
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United States. Federal Highway Administration (2022). Federal Highway Administration's Cooperative Driving Automation Program: Automated Vehicles Working Together (Report No. FHWA-HRT-22-039). United States. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/60760
United States. Federal Highway Administration. Federal Highway Administration's Cooperative Driving Automation Program: Automated Vehicles Working Together. Report no. FHWA-HRT-22-039. United States. Federal Highway Administration, 2022. https://rosap.ntl.bts.gov/view/dot/60760.
United States. Federal Highway Administration Federal Highway Administration's Cooperative Driving Automation Program: Automated Vehicles Working Together. United States. Federal Highway Administration, 2022, Report no. FHWA-HRT-22-039, ROSA P. https://rosap.ntl.bts.gov/view/dot/60760.
New York City is piloting connected vehicle (CV) technology to support the Vision Zero initiative and help eliminate injuries and fatalities caused by crashes. As a part of the USDOT CV Pilot Deployment Program, a Mobile Accessible Pedestrian Signal System (PED-SIG) was developed. The PED-SIG application provides audio alerts and haptic prompts to
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Ozbay, K., Gao, J., Zuo, F., Talas, M., Khanzada, H., Roth, G., Rausch, R., Benevelli, D., Sim, S., & Opie, K. (2021). Connected Vehicle Pilot Deployment Program Phase 3, Mobile Accessible Pedestrian Signal System (PED-SIG) – New York City Department of Transportation (NYCDOT) (Report No. FHWA-JPO-22-921). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/63614
Ozbay, Kaan, Jingqin Gao, Fan Zuo, Mohamad Talas, Hisham Khanzada, Gary Roth, Robert Rausch, David Benevelli, Samuel Sim, and Keir Opie. Connected Vehicle Pilot Deployment Program Phase 3, Mobile Accessible Pedestrian Signal System (PED-SIG) – New York City Department of Transportation (NYCDOT). Report no. FHWA-JPO-22-921. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021. https://rosap.ntl.bts.gov/view/dot/63614.
Ozbay, Kaan, et al. Connected Vehicle Pilot Deployment Program Phase 3, Mobile Accessible Pedestrian Signal System (PED-SIG) – New York City Department of Transportation (NYCDOT). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021, Report no. FHWA-JPO-22-921, ROSA P. https://rosap.ntl.bts.gov/view/dot/63614.
This white paper explores the applicability of cooperative driving for advanced connected vehicles (CD for ACV) on urban roadways based on insights, data analysis, and stakeholder feedback documented as a part of the USDOT Connected Vehicle Pilot Deployment (CVPD). Three testable use cases are identified and mapped for New York City (NYC) applicati
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Talas, M., Ozbay, K., Gao, J., Bhattacharyya, A., Rausch, R., Benevelli, D., & Sim, S. (2021). Connected Vehicle Pilot Deployment Program Phase 3, Understanding and Enabling Cooperative Driving for Advanced Connected Vehicles in New York City – New York City Department of Transportation (NYCDOT) (Report No. FHWA-JPO-21-920). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/63613
Talas, Mohamad, Kaan Ozbay, Jingqin Gao, Abhinav Bhattacharyya, Robert Rausch, David Benevelli, and Samuel Sim. Connected Vehicle Pilot Deployment Program Phase 3, Understanding and Enabling Cooperative Driving for Advanced Connected Vehicles in New York City – New York City Department of Transportation (NYCDOT). Report no. FHWA-JPO-21-920. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021. https://rosap.ntl.bts.gov/view/dot/63613.
Talas, Mohamad, et al. Connected Vehicle Pilot Deployment Program Phase 3, Understanding and Enabling Cooperative Driving for Advanced Connected Vehicles in New York City – New York City Department of Transportation (NYCDOT). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021, Report no. FHWA-JPO-21-920, ROSA P. https://rosap.ntl.bts.gov/view/dot/63613.
Emerging automated vehicles (AV) may be able to provide advanced information about the surrounding information with video cameras, radar sensors, lidar sensors, etc. Such information will enable estimating and predicting transportation system states on mobility, energy, and emissions. In this study, a physical informed neural network is developed t
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Li, X., Liang, Z., Shi, X., & Yao, H. (2021). Vehicle-based Sensing for Energy and Emission Reduction. Cornell University. Center for Transportation, Environment, and Community Health. (CTECH). https://rosap.ntl.bts.gov/view/dot/60816
Li, Xiaopeng, Zhaohui Liang, Xiaowei Shi, and Handong Yao. Vehicle-based Sensing for Energy and Emission Reduction. Cornell University. Center for Transportation, Environment, and Community Health. (CTECH), 2021. https://rosap.ntl.bts.gov/view/dot/60816.
Li, Xiaopeng, et al. Vehicle-based Sensing for Energy and Emission Reduction. Cornell University. Center for Transportation, Environment, and Community Health. (CTECH), 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60816.
A solar-powered automated transportation network (ATN) connecting the North and South campuses of San José State University with three passenger stations was designed, visualized, and analyzed in terms of its energy usage, carbon offset, and cost. The study’s methodology included the use of tools and software such as ArcGIS, SketchUp, Infraworks, S
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Furman, B., Ramasubramanian, L., McDonald, S., Swenson, R., Fogelquist, J., Chiao, Y., Pape, A., & Cruz, M. (2021). Solar-Powered Automated Transportation: Feasibility and Visualization (Report No. 21-29, CA-MTI-1948). San Jose State University. https://rosap.ntl.bts.gov/view/dot/60556
Furman, Burford, Laxmi Ramasubramanian, Shannon McDonald, Ron Swenson, Jack Fogelquist, Yu Chiao, Alex Pape, and Mario Cruz. Solar-Powered Automated Transportation: Feasibility and Visualization. Report no. 21-29, CA-MTI-1948. San Jose State University, 2021. https://rosap.ntl.bts.gov/view/dot/60556.
Furman, Burford, et al. Solar-Powered Automated Transportation: Feasibility and Visualization. San Jose State University, 2021, Report no. 21-29, CA-MTI-1948, ROSA P. https://rosap.ntl.bts.gov/view/dot/60556.
SAE Level 5 autonomy requires the autonomous vehicle to be able to accurately sense the environment and detect obstacles in all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions that include such events as sudden change in lighting, smoke, fog, snow, and rain. There is no standalone sens
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Singh, A. P., Vegamoor, V. K., & Rathinam, S. (2021). A Sensor Fusion and Localization System for Improving Vehicle Safety in Challenging Weather Conditions (Report No. 04-117). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/60996
Singh, Abhay P., Vamsi Krishna Vegamoor, and Sivakumar Rathinam. A Sensor Fusion and Localization System for Improving Vehicle Safety in Challenging Weather Conditions. Report no. 04-117. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/60996.
Singh, Abhay P., et al. A Sensor Fusion and Localization System for Improving Vehicle Safety in Challenging Weather Conditions. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 04-117, ROSA P. https://rosap.ntl.bts.gov/view/dot/60996.
Connected and Autonomous Vehicle (CAV) technologies enable communication among vehicles, and vehicles and infrastructure, paving the way for multiple safety and operational applications. This research developed and tested traffic signal control algorithms and control programs which utilized CAV-equipped heavy trucks and traffic signals. The focus o
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Zlatkovic, M., Ahmed, M. M., Cvijovic, Z., & Bashir, S. (2021). Connected-Autonomous Traffic Signal Control Algorithms for Trucks and Fleet Vehicles (Report No. WY2103F). Wyoming. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/58984
Zlatkovic, Milan, Mohamed M Ahmed, Zorica Cvijovic, and Sara Bashir. Connected-Autonomous Traffic Signal Control Algorithms for Trucks and Fleet Vehicles. Report no. WY2103F. Wyoming. Department of Transportation, 2021. https://rosap.ntl.bts.gov/view/dot/58984.
Zlatkovic, Milan, et al. Connected-Autonomous Traffic Signal Control Algorithms for Trucks and Fleet Vehicles. Wyoming. Department of Transportation, 2021, Report no. WY2103F, ROSA P. https://rosap.ntl.bts.gov/view/dot/58984.
Driverless vehicles must be self-aware to make learned and ethical decisions to avoid crashes in multimodal and diverse settings. This proposed effort will develop an Infrastructure Safety Support System by embedding vehicle-to-infrastructure (V2I) enabled sensor networks into the transportation infrastructure to provide autonomous vehicles and hum
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Huang, Y., Lu, P., Bridgelall, R., Yang, X., & Ren, Y. (2021). Infrastructure Safety Support System for Smart Cities with Autonomous Vehicles (Report No. MPC 21-447). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/61602
Huang, Ying, Pan Lu, Raj Bridgelall, Xinyi Yang, and Yihao Ren. Infrastructure Safety Support System for Smart Cities with Autonomous Vehicles. Report no. MPC 21-447. Mountain-Plains Consortium, 2021. https://rosap.ntl.bts.gov/view/dot/61602.
Huang, Ying, et al. Infrastructure Safety Support System for Smart Cities with Autonomous Vehicles. Mountain-Plains Consortium, 2021, Report no. MPC 21-447, ROSA P. https://rosap.ntl.bts.gov/view/dot/61602.
This project develops the main modules and algorithm models for the digital twin platform for a smart mobility testing ground currently under construction. LiDAR (Line Detection And Ranging)-sensor-based object detection and 3D infrastructure modeling modules are developed and tested in the project. The developed digital twin model is pilot tested
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Jin, P. J., Wang, Y., Zhang, T., Ge, Y., Gong, J., Chen, A., Ahmad, N. S., & Geng, B. (2021). The Development of the Digital Twin Platform for Smart Mobility Systems With High-Resolution 3D Data (Report No. CAIT-UTC-REG45). Rutgers University. Center for Advanced Infrastructure and Transportation. https://rosap.ntl.bts.gov/view/dot/65806
Jin, Peter J., Yizhou Wang, Tianya Zhang, Yi Ge, Jie Gong, Anjiang Chen, Noshin S Ahmad, and Bowen Geng. The Development of the Digital Twin Platform for Smart Mobility Systems With High-Resolution 3D Data. Report no. CAIT-UTC-REG45. Rutgers University. Center for Advanced Infrastructure and Transportation, 2021. https://rosap.ntl.bts.gov/view/dot/65806.
Jin, Peter J., et al. The Development of the Digital Twin Platform for Smart Mobility Systems With High-Resolution 3D Data. Rutgers University. Center for Advanced Infrastructure and Transportation, 2021, Report no. CAIT-UTC-REG45, ROSA P. https://rosap.ntl.bts.gov/view/dot/65806.
United States. Committee on the Marine Transportation System (CMTS)
2021-12-01
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The U.S. Committee on the Marine Transportation System (CMTS) in partnership with the Transportation Research Board (TRB) held the Sixth Biennial Marine Innovative Science and Technology Conference, “Advancing the Maritime Transportation System through Automation and Autonomous Technology: Trends, Applications, and Challenges,” virtually on March 1
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United States. Committee on the Marine Transportation System (CMTS) (2021). Advancing the Marine Transportation System through Automation and Autonomous Technologies: Trends, Applications, and Challenges: U.S. Committee on the Marine Transportation System 6th Biennial Innovative Science and Technology Conference. United States. Committee on the Marine Transportation System (CMTS). https://rosap.ntl.bts.gov/view/dot/60540
United States. Committee on the Marine Transportation System (CMTS). Advancing the Marine Transportation System through Automation and Autonomous Technologies: Trends, Applications, and Challenges: U.S. Committee on the Marine Transportation System 6th Biennial Innovative Science and Technology Conference. United States. Committee on the Marine Transportation System (CMTS), 2021. https://rosap.ntl.bts.gov/view/dot/60540.
United States. Committee on the Marine Transportation System (CMTS) Advancing the Marine Transportation System through Automation and Autonomous Technologies: Trends, Applications, and Challenges: U.S. Committee on the Marine Transportation System 6th Biennial Innovative Science and Technology Conference. United States. Committee on the Marine Transportation System (CMTS), 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60540.
Leveraging recent advancements in distributed optimization and reinforcement learning, and the growing connectivity and computational capability of vehicles and infrastructure, we propose to advance real-time adaptive signal control via distributed control and optimization. This report consists of three parts. Part 1 develops distributed algorithms
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Fei, X., Yu, X., Wang, X., Mi, T., Yin, Y., Shen, S., & Feng, Y. (2021). Real‐time Distributed Optimization of Traffic Signal Timing (Report No. Report No. 13). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/6942
Fei, Xinyu, Xiang Yu, Xingmin Wang, Tian Mi, Yafeng Yin, Siqian Shen, and Yiheng Feng. Real‐time Distributed Optimization of Traffic Signal Timing. Report no. Report No. 13. University of Michigan. Center for Connected and Automated Transportation, 2021. https://dx.doi.org/10.7302/6942.
Fei, Xinyu, et al. Real‐time Distributed Optimization of Traffic Signal Timing. University of Michigan. Center for Connected and Automated Transportation, 2021, Report no. Report No. 13, ROSA P. https://dx.doi.org/10.7302/6942.
Recently, 2D detection in images has made significant progress owing to the emergence of a convolutional neural network (CNN), which can extract high-level features from the images. However, detecting objects in 3D instead of 2D space is an essential topic when building perception systems for autonomous driving. An autonomous vehicle (AV) needs to
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DatasetSupporting Files
Wang, Y., Sun, W., & Liu, C. (2021). Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://doi.org/10.5281/zenodo.5738251
Wang, Yinhai, Wei Sun, and Chenxiao Liu. Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021. https://doi.org/10.5281/zenodo.5738251.
Wang, Yinhai, et al. Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021, ROSA P. https://doi.org/10.5281/zenodo.5738251.
Autonomous vehicles (AV) and advanced driver-assistance systems (ADAS) offer multiple safety benefits for drivers and road agencies. However, maintaining the lateral position of an AV or a vehicle with ADAS within a lane is a challenge, especially in adverse weather conditions when lane markings are occluded. For significant penetration of AV witho
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Dahal, S., & Roesler, J. R. (2021). Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle (Report No. ICT-21-039). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.36501/0197-9191/21-039
Dahal, Sachindra and Jeffery R. Roesler. Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle. Report no. ICT-21-039. University of Michigan. Center for Connected and Automated Transportation, 2021. https://doi.org/10.36501/0197-9191/21-039.
Dahal, Sachindra, and Jeffery R. Roesler Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle. University of Michigan. Center for Connected and Automated Transportation, 2021, Report no. ICT-21-039, ROSA P. https://doi.org/10.36501/0197-9191/21-039.
Automated Vehicles have been one of the most sought-after concepts to make transportation more effective and safer. No-occupant vehicles with automated driving systems (ADS) make up one such class of vehicles. These are primarily intended for goods transportation services. This vehicle class presents a body structure different than that of a passen
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Dobrovolny, C. S., Stoeltje, G., & Zalani, A. (2021). Crash Compatibility of Automated Vehicles with Passenger Vehicles (Report No. 05-098). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/61027
Dobrovolny, Chiara Silvestri, Gretchen Stoeltje, and Aniruddha Zalani. Crash Compatibility of Automated Vehicles with Passenger Vehicles. Report no. 05-098. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/61027.
Dobrovolny, Chiara Silvestri, et al. Crash Compatibility of Automated Vehicles with Passenger Vehicles. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 05-098, ROSA P. https://rosap.ntl.bts.gov/view/dot/61027.
This project proposes a modeling framework to integrate ride-sourcing services and connected/automated vehicles with transit to serve different users in an urban area. Multiple travel modes are considered for morning commute: single ride and shared ride in ride-sourcing, and integrated ride-sourcing (either single ride or shared ride) and transit.
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Ban, X. (., Wang, Y., MacKenzie, D., & Fan, R. (2021). Modeling and Optimizing Ride-sourcing Services in Connected and Automated Cities. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://rosap.ntl.bts.gov/view/dot/61335
Ban, Xuegang (Jeff), Yinhai Wang, Don MacKenzie, and Rong Fan. Modeling and Optimizing Ride-sourcing Services in Connected and Automated Cities. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021. https://rosap.ntl.bts.gov/view/dot/61335.
Ban, Xuegang (Jeff), et al. Modeling and Optimizing Ride-sourcing Services in Connected and Automated Cities. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/61335.
Rapidly evolving technology is changing the how and why of travel. The Alaska Department of Transportation and Public Facilities (DOT&PF) is preparing for these changes by developing a Connected and Automated Vehicle (CAV) Strategic Plan for the established Working Group, made up of stakeholders across Alaska. The CAV Strategic Plan centers on the
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Grosso, R., Dougherty, C., & Ooms, A. (2021). Connected & Automated Vehicle Working Group Strategic Plan (Report No. FHWA-AK-RD-000S(946)). Alaska. Department of Transportation and Public Facilities. Research and Technology Transfer. https://rosap.ntl.bts.gov/view/dot/60941
Grosso, Rachel, Claire Dougherty, and Andrew Ooms. Connected & Automated Vehicle Working Group Strategic Plan. Report no. FHWA-AK-RD-000S(946). Alaska. Department of Transportation and Public Facilities. Research and Technology Transfer, 2021. https://rosap.ntl.bts.gov/view/dot/60941.
Grosso, Rachel, et al. Connected & Automated Vehicle Working Group Strategic Plan. Alaska. Department of Transportation and Public Facilities. Research and Technology Transfer, 2021, Report no. FHWA-AK-RD-000S(946), ROSA P. https://rosap.ntl.bts.gov/view/dot/60941.
This brief summarizes an evaluation of the Federal Highway Administration's (FHWA's) investment in the truck platooning research project titled "Assessing the Feasibility of Deploying Partial Automation for Truck Platooning." This research project included two complementary subprojects, titled "Partial Automation for Truck Platooning" (PATP) and "D
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Foreman, C., Keen, M., Petrella, M., & Plotnick, S. (2021). FHWA Research and Technology Evaluation TechBrief: Truck Platooning (Report No. FHWA-HRT-22-007). United States. Federal Highway Administration. Research and Technology Evaluation. https://rosap.ntl.bts.gov/view/dot/60195
Foreman, Christina, Matthew Keen, Margaret Petrella, and Sarah Plotnick. FHWA Research and Technology Evaluation TechBrief: Truck Platooning. Report no. FHWA-HRT-22-007. United States. Federal Highway Administration. Research and Technology Evaluation, 2021. https://rosap.ntl.bts.gov/view/dot/60195.
Foreman, Christina, et al. FHWA Research and Technology Evaluation TechBrief: Truck Platooning. United States. Federal Highway Administration. Research and Technology Evaluation, 2021, Report no. FHWA-HRT-22-007, ROSA P. https://rosap.ntl.bts.gov/view/dot/60195.
Recent development of autonomous and connected trucks (ACT) has provided the freight industry with the option of using truck platooning to improve fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterior
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She, R., & Ouyang, Y. (2021). Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency (Report No. ICT-21-037;UILU-ENG-2021-2037). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.36501/0197-9191/21-037
She, Ruifeng and Yanfeng Ouyang. Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency. Report no. ICT-21-037;UILU-ENG-2021-2037. University of Michigan. Center for Connected and Automated Transportation, 2021. https://doi.org/10.36501/0197-9191/21-037.
She, Ruifeng, and Yanfeng Ouyang Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency. University of Michigan. Center for Connected and Automated Transportation, 2021, Report no. ICT-21-037;UILU-ENG-2021-2037, ROSA P. https://doi.org/10.36501/0197-9191/21-037.
As part of the City of Riverside’s Smart-City initiative, UC Riverside researchers have developed an Innovation Corridor testbed for enabling shared electric connected and automated transportation research. This Innovation Corridor testbed is located in Riverside California, and consists of a six-mile section of University Avenue between the UC Riv
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Dataset
Oswald, D., Hao, P., Williams, N., & Barth, M. (2021). Development of an Innovation Corridor Testbed for Shared Electric Connected and Automated Transportation [Supporting Dataset] (Report No. NCST-UCR-RR-21-20). National Center for Sustainable Transportation (NCST) (UTC). https://doi.org/10.6086/D1VH5W
Oswald, David, Peng Hao, Nigel Williams, and Matthew Barth. Development of an Innovation Corridor Testbed for Shared Electric Connected and Automated Transportation [Supporting Dataset]. Report no. NCST-UCR-RR-21-20. National Center for Sustainable Transportation (NCST) (UTC), 2021. https://doi.org/10.6086/D1VH5W.
Oswald, David, et al. Development of an Innovation Corridor Testbed for Shared Electric Connected and Automated Transportation [Supporting Dataset]. National Center for Sustainable Transportation (NCST) (UTC), 2021, Report no. NCST-UCR-RR-21-20, ROSA P. https://doi.org/10.6086/D1VH5W.
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