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.
Connected and Autonomous Vehicles (CAVs) have gained huge expectations in improving safety, efficiency, and environmental friendliness for transportation. However, they are still at a relatively early stage of development, and little attention has been paid to the readiness of roadway infrastructure. The focus of this report will be on infrastructu
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Jafari, M. A., Jin, P. J., Di, S., Huang, Y., & Wang, Y. (2020). Infrastructure Readiness for Electric, Connected and Automated Vehicles-Policies, Planning, and Pilot Testing on Infrastructure Readiness for Electrical, Connected, Automated, and Ridesharing Vehicles (Report No. CAIT-UTC-REG10). Center for Advanced Infrastructure and Transportation (CAIT) (UTC). https://rosap.ntl.bts.gov/view/dot/82862
Jafari, Mohsen A, Peter J. Jin, Sharon Di, Yufei Huang, and Yizhou Wang. Infrastructure Readiness for Electric, Connected and Automated Vehicles-Policies, Planning, and Pilot Testing on Infrastructure Readiness for Electrical, Connected, Automated, and Ridesharing Vehicles. Report no. CAIT-UTC-REG10. Center for Advanced Infrastructure and Transportation (CAIT) (UTC), 2020. https://rosap.ntl.bts.gov/view/dot/82862.
Jafari, Mohsen A, et al. Infrastructure Readiness for Electric, Connected and Automated Vehicles-Policies, Planning, and Pilot Testing on Infrastructure Readiness for Electrical, Connected, Automated, and Ridesharing Vehicles. Center for Advanced Infrastructure and Transportation (CAIT) (UTC), 2020, Report no. CAIT-UTC-REG10, ROSA P. https://rosap.ntl.bts.gov/view/dot/82862.
Autonomous vehicles (AVs) are rapidly emerging in United States cities, leaving urban and regional planning institutions unsure how to plan and develop policies. This paper analyzes how regional transportation plans (RTPs) developed by metropolitan planning organizations (MPOs) are approaching the risks and opportunities presented by AVs. Among 52
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Miller, T., Pendyala, R. M., McAslan, D., & Gabriele, M. (2020). Emerging Approaches to Autonomous Vehicles in Transportation Policy and Planning. Center for Teaching Old Models New Tricks (TOMNET). https://rosap.ntl.bts.gov/view/dot/74102
Miller, Thaddeus, Ram M. Pendyala, Devon McAslan, and Max Gabriele. Emerging Approaches to Autonomous Vehicles in Transportation Policy and Planning. Center for Teaching Old Models New Tricks (TOMNET), 2020. https://rosap.ntl.bts.gov/view/dot/74102.
Miller, Thaddeus, et al. Emerging Approaches to Autonomous Vehicles in Transportation Policy and Planning. Center for Teaching Old Models New Tricks (TOMNET), 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/74102.
The goal of this project is to improve mobility in urban street networks by developing a methodology for dynamic speed harmonization suitable for connected urban street networks. The methodology aims at finding optimal advisory speeds on each transportation link that will be transferred to connected and autonomous vehicles, with the objective of re
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Hajbabaie, A., & Tajalli, M. (2020). Dynamic Speed Harmonization in Connected Urban Street Networks: Improving Mobility (Report No. 2019 Project 17;CAMMSE-UNCC-2019-UTC-Project-17). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/57029
Hajbabaie, Ali and Mehrdad Tajalli. Dynamic Speed Harmonization in Connected Urban Street Networks: Improving Mobility. Report no. 2019 Project 17;CAMMSE-UNCC-2019-UTC-Project-17. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2020. https://rosap.ntl.bts.gov/view/dot/57029.
Hajbabaie, Ali, and Mehrdad Tajalli Dynamic Speed Harmonization in Connected Urban Street Networks: Improving Mobility. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2020, Report no. 2019 Project 17;CAMMSE-UNCC-2019-UTC-Project-17, ROSA P. https://rosap.ntl.bts.gov/view/dot/57029.
This research will develop guidelines and recommendations for estimating and predicting intersection efficiency in the presence of connected and autonomous vehicles (CAVs) and therefore will lead to a better understanding of how CAVs will improve mobility at signalized intersections. To better understand the impact of CAVs on the operation of signa
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Fan, W. (., & Liu, P. (2020). Trajectory Optimization of Connected and Autonomous Vehicles (CAVs) at Signalized Intersections. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/57057
Fan, Wei (David) and Pengfei Liu. Trajectory Optimization of Connected and Autonomous Vehicles (CAVs) at Signalized Intersections. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2020. https://rosap.ntl.bts.gov/view/dot/57057.
Fan, Wei (David), and Pengfei Liu Trajectory Optimization of Connected and Autonomous Vehicles (CAVs) at Signalized Intersections. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/57057.
In this report we address the problem of cooperative lane change maneuvers where vehicles communicate with each other and negotiate the creation of safe spacings in order to merge without taking any safety risks. The proposed approach requires that the merging vehicle negotiates the creation of a safety gap in the destination lane and till the lane
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Iannou, P., & Monteiro, F. V. (2020). Connected Autonomous Vehicles: Safety During Merging and Lane Change and Impact on Traffic Flow (Report No. CA20-3405). California. Dept. of Transportation. Division of Research and Innovation. https://rosap.ntl.bts.gov/view/dot/55510
Iannou, Petros and Fernando V Monteiro. Connected Autonomous Vehicles: Safety During Merging and Lane Change and Impact on Traffic Flow. Report no. CA20-3405. California. Dept. of Transportation. Division of Research and Innovation, 2020. https://rosap.ntl.bts.gov/view/dot/55510.
Iannou, Petros, and Fernando V Monteiro Connected Autonomous Vehicles: Safety During Merging and Lane Change and Impact on Traffic Flow. California. Dept. of Transportation. Division of Research and Innovation, 2020, Report no. CA20-3405, ROSA P. https://rosap.ntl.bts.gov/view/dot/55510.
This report documents the work completed by the Crash Avoidance Metrics Partners LLC (CAMP) Vehicle to Infrastructure (V2I) Consortia during the sixth year of the “Development of Vehicle-to-Infrastructure Applications (V2I) Program.” Participating companies in the V2I Consortia (V2I, V2I-2 and V2I-3) during this period were Ford, General Motors, Hy
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Shulman, M., & Geisler, S. (2020). Development of Vehicle-to-Infrastructure Applications Program: Sixth Annual Report (Report No. FHWA-JPO-20-819). United States. Department of Transportation. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/56499
Shulman, Michael and Scott Geisler. Development of Vehicle-to-Infrastructure Applications Program: Sixth Annual Report. Report no. FHWA-JPO-20-819. United States. Department of Transportation. Federal Highway Administration, 2020. https://rosap.ntl.bts.gov/view/dot/56499.
Shulman, Michael, and Scott Geisler Development of Vehicle-to-Infrastructure Applications Program: Sixth Annual Report. United States. Department of Transportation. Federal Highway Administration, 2020, Report no. FHWA-JPO-20-819, ROSA P. https://rosap.ntl.bts.gov/view/dot/56499.
Detection performance as a function of distance was measured for 16 subjects who pressed a button upon aurally detecting the approach of an electric vehicle. The vehicle was equipped with loudspeakers that broadcast one of four additive warning sounds. Other test conditions included two vehicle approach speeds [10 and 20 km/h (kph)] and two backgro
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Roan, M. J., Neurauter, L., Song, M., & Miller, M. (2020). Probability of Detection of Electric Vehicles with Added Warning Sounds. Virginia Tech Transportation Institute. https://rosap.ntl.bts.gov/view/dot/60346
Roan, Michael J, Luke Neurauter, Miao Song, and Marty Miller. Probability of Detection of Electric Vehicles with Added Warning Sounds. Virginia Tech Transportation Institute, 2020. https://rosap.ntl.bts.gov/view/dot/60346.
Roan, Michael J, et al. Probability of Detection of Electric Vehicles with Added Warning Sounds. Virginia Tech Transportation Institute, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/60346.
This report summarizes the results of a study conducted to document the safety and mobility needs of Rural, Isolated, Tribal, or Indigenous (RITI) communities and to identify autonomous and connected vehicle technology that have the potential of addressing these needs. A review of the administrative structure for the five Native American Tribes in
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Sorour, S., Abdel-Rahim, A., Swoboda-Colberg, S., & Hassan, M. (2020). Transportation Equity for RITI Communities in Autonomous and Connected Vehicle Environment: Opportunities and Barriers. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET). https://rosap.ntl.bts.gov/view/dot/58704
Sorour, Sameh, Ahmed Abdel-Rahim, Skye Swoboda-Colberg, and Mohamed Hassan. Transportation Equity for RITI Communities in Autonomous and Connected Vehicle Environment: Opportunities and Barriers. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2020. https://rosap.ntl.bts.gov/view/dot/58704.
Sorour, Sameh, et al. Transportation Equity for RITI Communities in Autonomous and Connected Vehicle Environment: Opportunities and Barriers. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/58704.
We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical er
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Li, T., Liu, L., Kyrillidis, A., & Caramanis, C. (2020). Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 1 (Report No. D-STOP/2020/159). University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP). https://rosap.ntl.bts.gov/view/dot/55860
Li, Tianyang, Liu Liu, Anastasios Kyrillidis, and Constantine Caramanis. Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 1. Report no. D-STOP/2020/159. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020. https://rosap.ntl.bts.gov/view/dot/55860.
Li, Tianyang, et al. Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 1. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020, Report no. D-STOP/2020/159, ROSA P. https://rosap.ntl.bts.gov/view/dot/55860.
This paper develops an analytic system to investigate the effects of AV availability on multiple dimensions of activity-travel behavior at once, based on a direct survey-based modeling approach. The model uses individual socio-demographics, built environment variables, as well as psycho-social variables (in the form of latent psychological construc
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Dannemiller, K. A., Mondal, A., Asmussen, K. E., & Bhat, C. R. (2020). Investigating Autonomous Vehicle Impacts on Individual Activity-Travel Behavior (Report No. D-STOP/2020/156). University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP). https://rosap.ntl.bts.gov/view/dot/55863
Dannemiller, Katherine A., Aupal Mondal, Katherine E. Asmussen, and Chandra R. Bhat. Investigating Autonomous Vehicle Impacts on Individual Activity-Travel Behavior. Report no. D-STOP/2020/156. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020. https://rosap.ntl.bts.gov/view/dot/55863.
Dannemiller, Katherine A., et al. Investigating Autonomous Vehicle Impacts on Individual Activity-Travel Behavior. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020, Report no. D-STOP/2020/156, ROSA P. https://rosap.ntl.bts.gov/view/dot/55863.
Reservation-based traffic control is a revolutionary intersection management system which involves the communication of autonomous vehicles and an intersection to request space-time trajectories through the intersection. Although previous studies have found congestion and throughput benefits of reservation-based control that surpass signalized cont
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Patel, R., Venkatraman, P., & Boyles, S. D. (2020). Optimal Placement of Reservation-Based Intersections in Urban Networks (Report No. D-STOP/2020/152). University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP). https://rosap.ntl.bts.gov/view/dot/56051
Patel, Rahul, Prashanth Venkatraman, and Stephen D. Boyles. Optimal Placement of Reservation-Based Intersections in Urban Networks. Report no. D-STOP/2020/152. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020. https://rosap.ntl.bts.gov/view/dot/56051.
Patel, Rahul, et al. Optimal Placement of Reservation-Based Intersections in Urban Networks. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020, Report no. D-STOP/2020/152, ROSA P. https://rosap.ntl.bts.gov/view/dot/56051.
Commuting congestion increases alongside the prosperity of urban cities. With the rapid development of ride sourcing services and the advances of the connected and automated vehicles (CAV), researchers are seeking innovative approaches to alleviate commuting congestion by integrating CAV-based ride sourcing and transit services. We propose a genera
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Fan, R., MacCabe, D., & Ban, X. (. (2020). A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute. University of Washington. Department of Civil and Environmental Engineering. https://rosap.ntl.bts.gov/view/dot/61341
Fan, Rong, Dan MacCabe, and Xuegang (Jeff) Ban. A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute. University of Washington. Department of Civil and Environmental Engineering, 2020. https://rosap.ntl.bts.gov/view/dot/61341.
Fan, Rong, et al. A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute. University of Washington. Department of Civil and Environmental Engineering, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/61341.
Deployment of automated ground vehicles beyond the confines of sunny and dry climes will require sub-lane level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust sub-5
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Narula, L., Iannucci, P. A., & Humphreys, T. E. (2020). All-Weather Sub-50-cm Radar-Inertial Positioning (Report No. D-STOP/2020/154). University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP). https://rosap.ntl.bts.gov/view/dot/55864
Narula, Lakshay, Peter A. Iannucci, and Todd E. Humphreys. All-Weather Sub-50-cm Radar-Inertial Positioning. Report no. D-STOP/2020/154. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020. https://rosap.ntl.bts.gov/view/dot/55864.
Narula, Lakshay, et al. All-Weather Sub-50-cm Radar-Inertial Positioning. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020, Report no. D-STOP/2020/154, ROSA P. https://rosap.ntl.bts.gov/view/dot/55864.
With the advent of automated vehicle systems, the role of drivers has changed to a more supervisory role. However, it is known that all vehicles with Level 2 (L2) systems have a very specific operational design domain (ODD) and can only function on limited conditions. Hence, it is important for drivers to perceive the situations properly and regain
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Roberts, S. C., & Ebadi, Y. (2020). Training to Improve Drivers’ Behavior When Partial Driving Automation Fails (Report No. UM-3-Y3). Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/61184
Roberts, Shannon C and Yalda Ebadi. Training to Improve Drivers’ Behavior When Partial Driving Automation Fails. Report no. UM-3-Y3. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2020. https://rosap.ntl.bts.gov/view/dot/61184.
Roberts, Shannon C, and Yalda Ebadi Training to Improve Drivers’ Behavior When Partial Driving Automation Fails. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2020, Report no. UM-3-Y3, ROSA P. https://rosap.ntl.bts.gov/view/dot/61184.
We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical er
...
Li, T., Liu, L., Kyrillidis, A., & Caramanis, C. (2020). Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 2 (Report No. D-STOP/2020/160). University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP). https://rosap.ntl.bts.gov/view/dot/55859
Li, Tianyang, Liu Liu, Anastasios Kyrillidis, and Constantine Caramanis. Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 2. Report no. D-STOP/2020/160. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020. https://rosap.ntl.bts.gov/view/dot/55859.
Li, Tianyang, et al. Statistical Inference Without Excess Data Using Only Stochastic Gradients: Volume 2. University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP), 2020, Report no. D-STOP/2020/160, ROSA P. https://rosap.ntl.bts.gov/view/dot/55859.
Automated vehicles (AVs) should be deployed gradually and geometrically selectively to ensure safety. Frequent collisions of AVs in certain driving scenarios, such as in dark streets or crowded areas, have posed wide concerns of AV safety. People want to know what kinds of driving circumstances or areas are easy for AVs and what are relatively hard
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Zhao, D., Xu, M., Chen, R., Arief, M., Zhang, W., & Wang, W. (2020). Labeling Roads with Different Types of Automated Driving Functional Requirements using Machine Learning. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/56109
Zhao, Ding, Mengdi Xu, Rui Chen, Mansur Arief, Weiyang Zhang, and Wenshuo Wang. Labeling Roads with Different Types of Automated Driving Functional Requirements using Machine Learning. Mobility21, Carnegie Mellon University, 2020. https://rosap.ntl.bts.gov/view/dot/56109.
Zhao, Ding, et al. Labeling Roads with Different Types of Automated Driving Functional Requirements using Machine Learning. Mobility21, Carnegie Mellon University, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/56109.
FTA has conducted research on conceptual ideas, prototypes, and commercially available products related to automated vehicle technologies for transit bus operations. The emerging automated transit bus market has received enthusiastic media coverage, but stakeholders may not clearly understand the difference between conceptual ideas, prototype syste
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Supporting Files
Cregger, J., Machek, E., & Cahill, P. (2020). Transit Bus Automation Market Assessment (Report No. DOT-VNTSC-FTA-19-03;FTA Report No. 0144). United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation. https://doi.org/10.21949/1506045
Cregger, Joshua, Elizabeth Machek, and Patricia Cahill. Transit Bus Automation Market Assessment. Report no. DOT-VNTSC-FTA-19-03;FTA Report No. 0144. United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation, 2020. https://doi.org/10.21949/1506045.
Cregger, Joshua, et al. Transit Bus Automation Market Assessment. United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation, 2020, Report no. DOT-VNTSC-FTA-19-03;FTA Report No. 0144, ROSA P. https://doi.org/10.21949/1506045.
This report presents a study that investigated potential travel behavior changes in light of automated, connected, electric, and shared-use vehicle (ACES) technologies. Three main aspects of choice behavior were investigated: AV adoption and willingness to pay (WTP), shared mobility adoption, and mode choice. Particularly, this study focuses on exp
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Jin, X., Rahimi, A., & Azimi, G. (2020). The Impacts of Emerging Mobility Options and Vehicle Technologies on Travel Behavior. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/54687
Jin, Xia, Alireza Rahimi, and Ghazaleh Azimi. The Impacts of Emerging Mobility Options and Vehicle Technologies on Travel Behavior. Florida Department of Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/54687.
Jin, Xia, et al. The Impacts of Emerging Mobility Options and Vehicle Technologies on Travel Behavior. Florida Department of Transportation, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/54687.
This study was designed to assess capacity changes due to the introduction of connected vehicles (CVs) and automated vehicles (AVs) on Virginia freeway corridors. Overall, three vehicle types, including legacy vehicles (LVs); vehicles equipped with adaptive cruise control (ACC) (AVs); and vehicles equipped with cooperative adaptive cruise control (
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Heaslip, K., Goodall, N. J., Kim, B., & Aad, M. A. (2020). Assessment of Capacity Changes Due to Automated Vehicles on Interstate Corridors (Report No. FHWA/VTRC 21-R1). Virginia. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/50850
Heaslip, Kevin, Noah J. Goodall, Bumsik Kim, and Mirla Abi Aad. Assessment of Capacity Changes Due to Automated Vehicles on Interstate Corridors. Report no. FHWA/VTRC 21-R1. Virginia. Department of Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/50850.
Heaslip, Kevin, et al. Assessment of Capacity Changes Due to Automated Vehicles on Interstate Corridors. Virginia. Department of Transportation, 2020, Report no. FHWA/VTRC 21-R1, ROSA P. https://rosap.ntl.bts.gov/view/dot/50850.
Disruptive changes in mobility services are emerging across the nation and in New York State that have the potential to further enable private car-optional lifestyles and expanded, more flexible and affordable mobility choices. By convening stakeholder engagement roundtables of advanced transportation experts, then utilizing available data for visu
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Kamga, C., Mudigonda, S., Thorson, E., Jegou, M., Sperling, J., Young, S., Wilson, A., Holden, J., Rames, C., Duvall, A., Zimny-Schmitt, D., & Romero-Lankao, P. (2020). Long-Term Impacts of Shared, Connected, Automated, and E-Mobility in New York State: Exploring Hubs of Mobility and Energy Innovation (Report No. 20-10). New York State Department of Transportation. https://rosap.ntl.bts.gov/view/dot/53989
Kamga, Camille, Sandeep Mudigonda, Ellen Thorson, Marine Jegou, Josh Sperling, Stan Young, and Alana Wilson, et al.. Long-Term Impacts of Shared, Connected, Automated, and E-Mobility in New York State: Exploring Hubs of Mobility and Energy Innovation. Report no. 20-10. New York State Department of Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/53989.
Kamga, Camille, et al. Long-Term Impacts of Shared, Connected, Automated, and E-Mobility in New York State: Exploring Hubs of Mobility and Energy Innovation. New York State Department of Transportation, 2020, Report no. 20-10, ROSA P. https://rosap.ntl.bts.gov/view/dot/53989.
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