CAV Platooning Modeled on a Fixed Signal-Controlled Superstreet Showed a Decrease in Traffic Delay by Up to 15.1 Percent.
Benefits to Traffic Delay and Fuel Consumption of Various CAV Models in a Simulated North Carolina Superstreet Environment were Analyzed.
Date Posted

Evaluating and Comparing the Impact of Connected and Autonomous Vehicles on Conventional Intersections and Superstreets

Summary Information

Various modeling frameworks have been proposed in the literature for Connected and Automated Vehicles (CAVs) in different traffic facilities. A research team at the Center for Advanced Multimodal Mobility Solutions and
Education (CAMMSE @ UNC Charlotte) aimed to evaluate the performances of CAVs at superstreet and conventional intersections by developing specific platooning, trajectory planning, and adaptive signal control models for CAVs. A "superstreet" is a variation of the median U-turn design, which guides left-turn vehicles from both the main street and minor street to travel through the intersection first and make a U-turn in a median opening, usually situated away from the main intersection. A superstreet located in Leeland, North Carolina was identified as a case study for its typical geometric design and traffic flow characteristics. The evaluation of CAVs’ performance was conducted by using a microscopic traffic simulation tool. Average traffic delay and fuel consumption were selected as the two performance measures.


The study used specific models for car following, platooning, trajectory planning, and adaptive signal control. Intelligent Driver Model (IDM) and Wiedemann 99 (W99) models were chosen to model the car following behavior of CAVs and Human Driver Vehicles (HDVs), respectively. The simulation analyses were conducted to demonstrate the operational performances of CAVs and HDVs in the environment of the superstreet and the equivalent conventional intersection in terms of average traffic delay and fuel consumption. This study tested four different traffic demand levels: 25, 50, 75, and 100 percent of the peak hour traffic volume. Also, a market penetration analysis was conducted on the 100 percent peak hour traffic volumes with 25, 50, and 75 percent of CAV market penetration rates. Two scenarios were considered: 1) platooning control and trajectory planning with fixed signal timing, and 2) platooning control and adaptive signal control signal timing.


Platooning and Trajectory Planning Control at Fixed Signal Timing

  • Platooning, trajectory planning, and platooning-based trajectory planning reduced traffic delay in most scenarios. The only scheme which did not reduce delays was platooning CAVs at 25 percent due to the speed of following vehicles being influenced by the leading vehicle and not being able to achieve maximum speed in scenarios with light roadway demand.
  • For the equivalent conventional intersection, the scenario that resulted in the greatest reduction in average traffic delay was for IDM with platooning-based trajectory planning, which under high demand (100 percent traffic demand) decreased delay from 33.74 to 23.82 seconds, a 29.4 percent reduction.
  • For the superstreet, CAVs with platooning-based trajectory planning at 100 percent traffic demand reduced average traffic delay from 23.32 to 19.80 seconds, a 15.1 percent reduction.
  • For the equivalent conventional intersection, IDM with platooning reduced fuel consumption under high demand scenarios by up to 15.2 percent (from 105.81 milliliters to 89.76 milliliters).
  • Similarly, for superstreet, IDM with platooning reduced fuel consumption under high demand scenarios by up to 11.8 percent (from 97.41 milliliters to 85.87 milliliters)

Platooning Control and Adaptive Signal Control

  • Adaptive signal control with CAVs yielded the largest improvement compared to trajectory planning and platooning in terms of both traffic delay and fuel consumption. The improvements increased with increased traffic demand levels.
  • Platooning control yielded traffic delay and fuel consumption benefits, and the highest improvement was more than 30 percent in terms of traffic delay at 100 percent traffic demand level.
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