CV/AV Resources

AMPO Connected and Automated Vehicle (CV/AV) Working Group Resources

Metropolitan Planning Organizations (MPOs), State Departments of Transportation (State DOTs), and other transportation agencies

The Travel Model Improvement Program has released a report on a peer exchange conducted in June 2019 to discuss Automated Vehicle Modeling by Metropolitan Planning Organizations. The report may be accessed at this link

With increasing interest in AVs and the potential impacts they stand to have on the Nation’s transportation network, many transportation agencies, including Metropolitan Planning Organizations (MPOs), are actively exploring ways to address AV modeling considerations in the long-range transportation planning process. The peer exchange convened representatives of five MPOs to discuss their approaches to AV modeling:

  • Maricopa Association of Governments (MAG) (Phoenix, AZ)
  • Mid-America Regional Council (MARC) (Kansas City, MO)
  • North Central Texas Council of Governments (NCTCOG) (Arlington, TX)
  • Sacramento Area Council of Governments (SACOG) (Sacramento, CA)
  • San Diego Association of Governments
  • (SANDAG) (San Diego, CA)

Key findings of the peer exchange include the following:

  • There is tremendous uncertainty about the impacts AVs will have on future transportation systems. Behavioral travel models are among the few tools that may provide useful mechanisms for conceptualization of the effect of these technologies.
  • Using models can allow for more informed decision making, recognizing that models provide insights and not answers. Every model has underlying assumptions and data. Models are a way to quantify the future and can provide a consistent set of metrics to explore scenarios reflecting various assumptions (e.g., land use, socioeconomic data, etc.).
  • Scenario testing can make the most of AV modeling compared to other more specific forecasting techniques due to the approach’s flexibility in exploring outcomes of multiple, varied futures.
  • Organizing estimates of “uncertainty” factors in the modeling chain by category of assumptions could help MPO modelers better understand how the factors relate and potentially impact each other.
  • Presenting model results and related information should include consequential assumptions about the model and external inputs. This method of communication provides a meaningful context for interpretation of the model results. In addition, it is important to keep the target audience in mind so that the model’s underlying assumptions and data can be communicated effectively.
  • Data interpretation is the foundation of travel modeling and is a significant skillset needed for modeling professionals. New data, new applications, and new data management approaches are emerging from AVs and other new technologies

-Contra Costa Transportation Authority and its partners lead a collaborative effort GoMentum Station, “the nation’s largest secure testing facility for autonomous and connected vehicle technology.”

-Florida DOT’s (FDOT) Automated Vehicles Initiative is “helping to create the framework for implementation by engaging stakeholders, developing research and pilot projects, and creating awareness of the technologies and how they support FDOT’s vision statement.”

-Miami-Dade MPO’s Connected-Automated Vehicle Program and Task Force is “working alongside its transportation partners to plan for this new technology with upgraded roadways, transit systems, and freight facilities that will move our community into the future.”

-Michigan DOT’s Connected Vehicles Program includes a working group, Vehicle to Infrastructure (V2I) Applications, Connected Vehicle Infrastructure Investments, Data Management, research, its Mobility Transformation Center, and a Safety Pilot Model Deployment with the U.S. Department of Transportation (U.S. DOT).,1607,7-151-9621_11041_38217—,00.html

Pennsylvania DOT’s CV/AV 2040 Vision.

-Nevada DOT is developing an Automated and Connected Vehicle Policy Framework.

-San Diego Association of Governments (SANDAG):  The San Diego region has been designated as one of the USDOT’s proving grounds for autonomous vehicles.

-Southeast Michigan Council of Governments held half-day conference on CV/AV:  Reimagining Transportation: Transforming Southeast Michigan



-Vehicle-to-Infrastructure (V2I) webpage:

-Connected Vehicle Pilot Deployment Program

-Connected Vehicle Test Beds

-Connected Vehicle Reference Implementation Architecture (CVRIA)

-USDOT/NHSTA Automated Vehicle Policy Guidance (NHTSA 2016-0091)

-NHTSA Automated Vehicles webpage

-NHTSA 2016-0126:  Vehicle to Vehicle (V2V) Communication Technology Notice of Proposed Rulemaking 1/12/2017

-NHTSA 2016-0040: NHTSA Enforcement Guidance Bulletin 2016-02 on Safety-Related Defects and Automated Safety Technologies Final Notice 9/23/2016

Other Agencies

-American Association of Motor Vehicle Administrators (AAMVA) Automated Vehicles Information Sharing Group, Automated Vehicle Working Group, and Automated Vehicle Information Library.

-American Association of State Highway and Transportation Officials (AASHTO) Autonomous Vehicle Policy Workshops, CV/AV Executive Leadership Team, Subcommitee on Transportation System Management and Operations (STSMO) CV/AV Working Group, CV/AV Research Roadmap and related tasks, CV Field Infrastructure Near Term Analysis Tool, and CV Field Infrastructure Footprint Analysis.

-National Conference of State Legislatures (NCSL) autonomous vehicle legislative information and database.

-National Operations Center of Excellence (NOCoE) Vehicle to Infrastructure Deployment Coalition (V2I DC) and Preparing for Connected/Automated Vehicles Webinar and

-RAND Corporation’s Automated Vehicle Technology: A Guide for Policymakers.

-SAE International Standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems identifies six levels of driving automation and has been adopted by the National Highway Traffic Safety Administration (NHTSA) and the USDOT’s Policy on Automated Vehicles.

-Transportation Research Board (TRB)

o NCHRP 20-24(98): CV/AV Research Roadmap and NCHRP 20-102: Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies.

o NCHRP Legal Research Digest 69: A look at the Legal Environment for Driverless Vehicles.

o Regional Transportation Systems Management and Operations (RTSMO) Committee (AHB10) Subcommittee on Connected and Automated Vehicles.

o Automated and Connected Vehicles University Transportation Center Spotlight Conference November 4-5, 2015.

 -Uniform Law Commission (ULC) draft model legislation for self-driving vehicles and Study Committee on State Regulation of Driverless Cars

-Victoria Transportation Policy Institute (VTPI) Autonomous Vehicle Implementation Predictions: Implications for Transport Planning.


University Affiliated

-Texas A&M Transportation Institute (TTI) CV/AV test beds, CV/AV physical infrastructure projects and transportation applications, commercial truck platooning demonstration project, policy research and planning studies. and

-University of Maryland Center for Advanced Transportation Technology Laboratory (CATT Lab) Automated Small Vehicle Transportation Research. ttp://

-University of Virginia Center for Transportation Studies Connected Vehicle/Infrastructure University Transportation Center (CVI UTC) Consortium and Connected Vehicle Pooled Fund. and



-Google:  The Waymo self-driving technology company evolved from  Google’s self-driving car. The project began in 2009 with the Toyota Prius. In 2012, the Lexus RX450h was added and in 2014, they designed a prototype vehicle, which began testing on public roads in 2015.

-INRIX’s Automated Vehicle Study identifies the top U.S. cities for shared highly autonomous vehicle (HAV) deployment, discusses how cities can leverage data to plan for HAVs, and discusses the data analysis needed to help public sector stakeholders strategically bring this technology to market.

-Tesla:  “All Tesla vehicles produced in…[their] factory, including Model 3, have the hardware needed for full self-driving capability.”

-Toyota Research Institute (TRI) Autonomous Vehicle Research Centers have partnerships with Stanford University, the Massachusetts Institute of Technology, and the University of Michigan MCity.,, and 


In The News

-Consumer Reports Self-Driving Cars: Driving Into the Future

-Wired: We Take a Ride in the Self-Driving Uber Now Roaming Pittsburgh