United States Department of Transportation
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Development of an Analysis/Modeling/Simulation (AMS) Framework for V2I and Connected/Automated Vehicle Environment
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2018-10-18
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[PDF-5.17 MB]
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Alternative Title:Development of an AMS Framework for Connected and Automated Vehicle Systems
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Corporate Contributors:United States. Federal Highway Administration ; United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
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Edition:Modeling Framework
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Abstract:This project developed a conceptual framework for an analysis, modeling, and simulation system for evaluating the impacts of connected and automated vehicle (CAV) technologies on transportation facilities at the strategic and operational levels, providing the basis for future development of CAV-enabled evaluation tools. The objective of this project is twofold: (1) to lay a foundational framework for the development of AMS system that includes connected and automated vehicles, and (2) to engage in small scale CAV AMS development using this framework that encourages future development activities as a step toward the availability of CAV-aware tools for practitioners. The aforementioned framework includes four main components that provide the core for an envisioned CAV AMS system for evaluating the strategic and tactical impacts of CAVs: 1) demand changes, 2) supply changes, 3) operational performance, 4) and network integration. To conduct a proof-of-concept test of a prototype CAV AMS framework, a case study focusing on the operational performance impacts of CAV systems was selected. The case study focuses on the performance impacts of CAV systems in a mixed traffic environment and uses an integrated traffic-telecommunication microsimulation tool that was developed at Northwestern University as a testbed. Using the aforementioned testbed, three sets of scenarios were evaluated. Mixed traffic flow simulations show that connectivity and automated driving can improve traffic flow throughput, stability, and travel time at high market penetrations. AV sensor performance simulations show that distance measurement error has insignificant impact on the performance of traffic flow in the case of low AV market penetration. High distance measurement error (30 percent), however, could lead to a small increase in throughput at high AV market penetrations, though this would depend on the programmed following distances specified for the AVs. Automated truck platooning simulations show that truck platoons formed under the assumed opportunistic platoon formation strategy are of small size (2-4 vehicles) and short duration (mostly less than 50 sec) in the testbed under consideration, as connected trucks activate platooning behavior only if they are following other connected trucks.
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