Utilization of Connectivity and Automation in Support of Transportation Agencies’ Decision Making, Phase II
Research Team
Mohammed Hadi, Ph.D., Florida International University
Shaoib Samandar, Ph.D., North Carolina State University/ITRE
Nagui Rouphail, Ph.D., North Carolina State University/ITRE
Angshuman Guin, Ph.D., Georgia Institute of Technology
Mike Hunter, Ph.D., Georgia Institute of Technology
Lily Elefteriadou, Ph.D., University of Florida
What is the issue? Advanced technology vehicles (ATV) including connected vehicles (CV), non-connected automated vehicles (AV), and connected and automated vehicle (CAV) technologies and applications promise transformative changes in transportation system performance. Agencies need the capability to assess the planning, design, operations, and management implications of the presence of such vehicles with different levels of connectivity and automation on system performance. In addition, agencies need to assess the impacts of these technologies as part of their decision-making processes to plan, design, operate, and manage the transportation system. Phase 1 of this project provided a basis to assist agencies that are considering the use of simulation for CAV analysis. The Phase 1 effort produced a comprehensive review of the state of practice in CAV simulation, an identification of the needs of transportation agencies in analyzing CAVs based on stakeholder inputs, guidance and framework to enable the simulation of CAV, and initial examples of utilizing microscopic simulation models to model CAV applications.
What will this project accomplish? The proposed Phase 2 of the project will build on the achievement of Phase 1 by applying the framework and guidance developed in Phase 1. Phase 2 will also involve further identification, development, and refinement of methods and models for evaluating CAV operations and their interactions with traditional vehicles and the infrastructure. Phase 2 will demonstrate the use of the guidance developed in Phase 1 by applying it to use cases in the Southeast region. The research team will select three case studies to apply the framework, methods, and algorithms identified and/or developed in the project. The research team will continue the involvement with the public and private agency stakeholders to identify use cases and to facilitate the adoption of the developed methods.