Evaluating and Comparing the Impact of Connected and Autonomous Vehicles on Conventional Intersections and Superstreets
Connected and Autonomous Vehicles (CAVs) have been one of the most promising technologies that are expected to bring significant changes to the transportation infrastructures. One of the main features of CAVs is that they can travel on roads without human intervention, and by doing so, traffic crashes caused by human errors can be eliminated and significant economic benefits can be reaped. Also, with the capabilities to communicate with surrounding vehicles and infrastructures, CAVs can travel through road junctions and segments more smoothly and efficiently, which will, in turn, increase the road capacity and reduce fuel consumption. To become better prepared for this transition, transportation engineers and researchers have begun to evaluate how well CAVs can perform in existing transportation infrastructures, such as freeways, on/off ramps, intersections, and roundabouts. Nevertheless, there have been few studies that have assessed the performance of CAVs in the environment of innovative intersections. The main goal of this research is to mitigate this research gap by conducting a simulation-based study to examine the operational performance of superstreets, one of the popularly implemented innovative intersection designs. This research intends to answer the following questions: (1) How the operational performances may vary between conventional intersections and superstreets in human-driven vehicles. (2) How CAVs perform in different traffic conditions. (3) At what market penetration rate do the CAVs start to improve the traffic efficiency. (4) How the performances of CAVs may differ when CAVs are enabled with different levels of capabilities. By answering the questions above, this research can provide a better understanding of the performance of CAVs at innovative intersections since many innovative intersections share similar design features such as displaced left turns and channelized right of way.
Language
- English
Project
- Status: Completed
- Funding: $90007
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Contract Numbers:
69A3551747133
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Sponsor Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of North Carolina - Charlotte
9201 University City Blvd
Charlotte, North Carolina United States 28223-0001 -
Project Managers:
Fan, Wei
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Performing Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223 -
Principal Investigators:
Fan, Wei
- Start Date: 20211001
- Expected Completion Date: 20220930
- Actual Completion Date: 20220930
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Connected vehicles; Highway capacity; Highway design; Highway safety; Impacts; Intersections; Market penetration; Methodology; Mobility; Traffic congestion; Traffic simulation
- Identifier Terms: VISSIM (Computer model)
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01784127
- Record Type: Research project
- Source Agency: Center for Advanced Multimodal Mobility Solutions and Education
- Contract Numbers: 69A3551747133
- Files: UTC, RIP
- Created Date: Oct 4 2021 10:54AM