Simulation Results Showed That Stop Sign Gap Assist Applications for Connected and Automated Vehicles Can Improve Capacity on Minor Approaches by up to 35.5 Percent; However, There was a Clear Trade-Off Between Capacity and Safety Benefits.

Researchers Simulated Impacts of Various Connected and Automated Vehicles (CAV) Enabled Technologies in Advanced Technology Vehicles on Roadway Safety and Capacity.

Date Posted
10/20/2022
Identifier
2022-B01685
TwitterLinkedInFacebook

Utilization of Connectivity & Automation in Support of Transportation Agencies’ Decision Making

Summary Information

Advanced technology vehicles (ATV) include connected vehicles (CV), automated vehicles (AV), connected and automated vehicles (CAV), and associated applications. This study was conducted to evaluate the existing information and research on ATV simulation to increase the efficacy to which future models and data sources from ATV technology can be developed. Specifically, the needs of public agencies in the southeastern United States were identified by conducting virtual focus groups with public agency stakeholders to understand the agencies’ needs with respect to ATV modeling. Questions were posed to query various aspects of agency need, such as study scopes, current ATV activities and efforts, future ATV plans, barriers to future ATV usage, and challenges in the adoption of ATV technology.

 METHODOLOGY

Several existing case studies were investigated to assess existing models and simulation development methods, document the data needs of agencies dealing with ATVs, general guidance regarding the usage of ATVs, and under what highway operation scenarios the usage of ATVs may be beneficial. These case studies presented in this study utilized microscopic simulation models for various CV, AV, and CAV applications. The case studies were categorized as arterial safety, arterial mobility, and freeway mobility applications, which included Red-Light Violation Warning (RLVW), Stop Sign Gap Assist (SSGA) and Signalized Left Turn Assist (SLTA), simulating signal control optimization, dedicated freeway lanes, etc. To quantify the safety benefits, vehicle trajectories were post-processed using the Surrogate Safety Assessment Model (SSAM).

FINDINGS

  • Based on the case study, SSGA could improve minor approach capacity by approximately 35.5 percent; however, the capacity increase was dependent on the SSGA gap time parameter, which may negatively impact safety. There was a clear trade-off between capacity benefits.
  • CV-based RLVW could increase safety by 50.7 percent at signalized intersections by reducing rear-end and right-angle collisions.
  • SLTA could increase left turning capacity up to 64.8 percent utilization with a gap time parameter of three seconds. Furthermore, the average delay for all vehicles can be reduced by approximately 58.4 percent. With a five-second gap time, SLTA decreased crossing conflicts from six per hour to zero.
  • AVs were found to have insignificant impact on capacity. In total, the presence of AVs reduced capacity by only one to two percent, depending on the scenario.
  • Reserving a lane for CAVs on a three-lane directional freeway was only beneficial when the CAV market penetration rate was between 20 percent and 60 percent, being most optimal at 40 percent. Outside of this range, a CAV dedicated lane increased congestion in all lanes. It was found that ramp volume level was the most important factor in this effect.
  • Optimal CAV weaving trajectory algorithms may increase the average speed of a system by 16 percent under low and medium demand conditions, and up to 11 percent under high demand. Furthermore, trajectory optimization could improve travel times by up to 17 percent, 30 percent, or 38 percent for 1.7 second, 1.4 second, or 1.0 second headways, respectively.
Goal Areas
Results Type
Deployment Locations