Deploy Traffic Optimization Applications for Connected and Automated Vehicles at Moderate or High Traffic Volume and Delay Signalized Intersections to Maximize Its Effectiveness.

Traffic Optimization for Signalized Corridors Were Evaluated Using Connected and Automated Vehicle (CAV) Traffic Simulations Under Varying Operating Conditions and Scenarios.

Date Posted
05/31/2022
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Identifier
2022-L01118

Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: Traffic Optimization for Signalized Corridors—Case Studies in Ann Arbor, MI, and Conroe, TX

Summary Information

Traffic Optimization for Signalized Corridors (TOSCo) is a series of innovative connected and automated vehicle applications designed to optimize traffic flow and minimize stopping on signalized arterial roadways. Real-time infrastructure information about queues and traffic signal operations are used by TOSCo equipped vehicles to plan and control their speeds to enhance mobility and reduce emissions across the corridor. This study investigated the mobility and environmental benefits of deploying TOSCo in one low and one high-speed corridor. The low-speed corridor was the 3.9-mile-long Plymouth suburban corridor, located in Ann Arbor, Michigan, which consisted of 11 intersections (nine arterial and two freeway interchanges). The high-speed corridor was the 12-mile-long stretch of SH 105, located in Conroe, Texas, which consisted of 15 intersections. Microscopic simulation models of the two selected suburban corridors were used to examine the potential mobility and environmental benefits of using TOSCo under different market penetration rates.

The following are the recommendations gathered based on the simulation analysis of TOSCo equipped vehicles conducted at the two suburban corridors. 

  • Deploy TOSCo at Moderate or High Traffic Volume and Delay Signalized Intersections to Maximize Its Effectiveness. Results from both corridors show that TOSCo was less effective at low traffic volume and low delay intersections. These locations leave very limited space for adjusting vehicle trajectories as most of the vehicles do not need to stop or slow down at such intersection. Low traffic volume on the side streets may also generate inaccurate SPaT information. Therefore, it is not suggested to activate the TOSCo function for those intersections with minimal benefits.
  • Select TOSCo parameters that match the corridor characteristics. In a mixed-traffic condition, differences in surrounding traffic can have a great impact on TOSCo vehicle behavior. Vehicles in a high-speed corridor have more aggressive speed profiles compared to TOSCo vehicles in low-speed profiles making it difficult for them to catch up due to limitations in acceleration settings. TOSCo parameters such as maximum acceleration and cooperative cruise control (CACC) set speed should be selected in such a way that they match the corridor characteristics and driving behavior.
  • Select algorithms that provide desired behavior in both low speed and high-speed corridors. TOSCo vehicles need to utilize profiles that accelerate differently. Acceleration from a stop should incorporate a buildup of the acceleration, constant acceleration, and a reduction of acceleration so that a TOSCo vehicle is able to reach desired speed in a reasonable amount of time. 
  • Account for unexpected queues and vehicle lane changing behavior. It is important that TOSCo vehicles are coded to accommodate manually driven vehicles that can change lanes in front of them.
  • Examine the impact of posted speed limit constraints. Speeds in all modes of TOSCo, except for the free-flow region mode, were limited to the posted speed limit. The mobility benefits may have been underestimated due to this constraint as TOSCo operations are compared to the baseline traffic (which is not the posted speed limit).
  • Account for road characteristics and acceleration changes in the TOSCo vehicle algorithm. The vehicle algorithm needs to account for the following: non-trivial initial acceleration for trajectory planning, inclusion of road-grade change, customization of different power-train characteristics, imperfection of sensors such as with global positioning systems and communications.

Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: Traffic Optimization for Signalized Corridors—Case Studies in Ann Arbor, MI, and Conroe, TX

Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: Traffic Optimization for Signalized Corridors—Case Studies in Ann Arbor, MI, and Conroe, TX
Source Publication Date
09/01/2021
Author
Huang, et.al.
Publisher
Prepared by Leidos for the U.S. DOT
Other Reference Number
Report No. FHWA-HRT-21-085
Goal Areas
System Engineering Elements

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