Simulation Study of a North Carolina Superstreet Intersection Estimates That Connected and Automated Vehicles with Platooning Control Could Reduce Fuel Consumption Up to 25 Percent.
Microsimulation Analysis Used to Model Impacts of Connected and Automated Vehicle Control Strategies for an Innovative Intersection in Leland, North Carolina.
Made Public Date
04/27/2022

Leland, NC

Leland, North Carolina,
United States
Identifier
2022-01644
TwitterLinkedInFacebook

Impact of Connected and Autonomous Vehicles on Nontraditional Intersection Design: Superstreets

Summary Information

Developments in wireless communication and artificial intelligence technologies have shown potential for increasing road capacity with Connected and Automated Vehicles (CAVs). Researchers used a simulation-based approach to further explore the impact of CAVs on the operational performance of innovatively designed intersections known as superstreets or restricted crossing U-turn intersections, which have fewer conflict points than traditionally designed intersections. A superstreet network from Leland, North Carolina was modeled in the SUMO microsimulation platform using previously collected traffic volume data.

Methodology

In this study, CAVs were assumed to be able to adjust their speeds based on the Signal Phasing and Timing (SPaT) information provided by infrastructure. Different traffic volumes, including 25, 50, 75, and 100 percent of peak demand, and CAV market penetration scenarios ranging from 0 to 100 percent, were modeled. Each scenario was run ten times in the simulation, with each run representing one hour of traffic. The operational performance was evaluated by two measures, fuel consumption and traffic delays. Three control strategies were examined, base control, platoon control, and platoon with trajectory planning control. In the base control scenarios, the superstreet network is filled with human-driven traffic only. In the platoon control scenarios, the superstreet network includes CAVs which are enabled with platoon capability only, or both platooning and trajectory planning capabilities.

Findings

  • Results from microsimulation analysis indicate that CAVs with platooning control can reduce fuel consumption, ranging from four percent to 25 percent across demand levels, with peak savings when the demand level was at 75 percent. However, platooning control did not have a significant benefit in terms of traffic delay. Delays were reduced at the 75 percent demand level but increased in the lower demand scenarios.
  • Comparing the scenario adding trajectory planning capabilities with the platooning only case, the highest reduction in fuel consumption (4 percent) and delays (10 percent) were observed at the 100 percent demand level, which represented congested conditions. 
  • At the 100 percent demand level, fuel consumption reductions were highest (10 percent) at 75 percent CAV market penetration, and delay reductions were greatest (10 percent) at a 100 percent CAV market penetration rate. However, at the 50 percent demand level, results were mixed, with increases in delay of up to 7 percent over the base scenario, and fuel consumption changes ranging from a reduction of 7 percent to an increase of 2 percent.
Results Type
Deployment Locations