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Intelligent Transportation Systems
Joint Program Office

Analysis, Modeling, and Simulation (AMS)

Analysis, Modeling and Simulation (AMS) provides tools and methodologies to evaluate the impacts of transportation technologies and strategies before deployment, enabling data-driven decision making.

Goal

Crosscutting research and resource development including design, planning, operations, and impact assessments informed by improved AMS.

Approach

Engage stakeholders to prioritize AMS capabilities for ITS, collaborate on research and development to enhance these capabilities, and develop resources to support their understanding and use.

Anticipated Outcomes

A robust and reliable set of ITS specific AMS tools and resources for better transportation decision-making and impact assessments.

Near-Term Actions

Conduct foundational research and development, collect, integrate and manage data, enhance models, perform case studies and testing, and facilitate outreach and knowledge transfer.

Current Project Highlights

Understanding the Costs Associated with Connected and Automated Vehicles

The objectives of this project are to
  1. Update the 2018 CAV Cost Tool using Volpe independent review recommendations and to account for new data surrounding the likely cost of CAVs (e.g., the recommendations in the 2021 FHWA Impacts of Automated Vehicles on Highway Infrastructure report; digital infrastructure costs, any cost changes associated with the Federal Communications Commission (FCC) safety band decision, etc.)
  2. Update the tool to be more flexible with scenario definitions so that state and local agencies can use the tool to explore costs associated with the scenario definitions they've developed with their stakeholders.

AMS Framework for Automated Trucks

Develop an AMS framework that is specifically designed for connected and automated trucks given the unique aspects of connected and automated trucks compared to other general road users.

Acquiring Baseline and ADAS-equipped Commercial Freight Driving Performance Datasets

Improve current AMS capabilities in assessing the performance of connected and automated trucks (CAT) by collecting/acquiring and assembling comparable CAT datasets. Datasets generated from this project, including both baseline data from human-driven vehicles and data from ADAS-driven trucks, will support the development of new CAT operations models that will allow for more realistic assessment of CAT impacts on traffic system performance.