A Case Study Estimated That With a 40 Percent CACC Market Penetration Rate Traffic Would Flow at Higher Speeds on SR 99 in California.

A simulation model examined the impacts of cooperative adaptive cruise control (CACC) at various market penetration rates.

Date Posted
04/30/2021
Identifier
2021-B01554
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Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California

Summary Information

Researchers conducted a simulation-based case study on the northbound side of a 13-mile segment of a busy urban corridor, with 16 on-ramps, 12 off-ramps and 10 interchanges (the SR 99 corridor in California). The goal of the case study was to investigate the effectiveness of Society of Automotive Engineers (SAE) Level 1 automation technology for mitigating transportation problems related to congestion, fuel consumption, and emissions.

The case study examined the impacts of passenger vehicles equipped with cooperative adaptive cruise control (CACC) on traffic performance and fuel consumption under various CACC market penetration scenarios, traffic demand inputs, and CACC management strategies. Additionally, it analyzed CACC string operation after vehicle awareness device (VAD) and CACC managed lane (ML) strategies were implemented. VAD vehicles have wireless communication capability and can broadcast real-time information regarding their operational status and route choice. The CACC ML strategy allows CACC vehicles and VAD vehicles to enter the ML, which physically separates the CACC traffic stream and the regular traffic stream.

METHODOLOGY



This case study used microscopic traffic simulation models to depict the impacts of CACC. The modeling framework was based on a state-of-the-art human-driver model and an adaptive cruise control (ACC)/ CACC car-following model, calibrated using ACC and CACC trajectory data obtained in field tests. The research team examined average vehicle speed, average miles per gallon (mpg) of fuel consumption, average string length, and CACC vehicle string probability (i.e., the probability of a CACC vehicle operating in a string) at various CACC market penetration rates (i.e., baseline, 20 40, 60, 80, and 100 percent). In addition, different traffic demands, (i.e., current demand, 120 percent of current demand, 130 percent of current demand, and 140 percent of current demand) and different CACC management strategies (i.e., VADs and CACC and ACC MLs) were studied.

FINDINGS

  • A CACC market penetration rate of 40 percent or higher showed traffic flowed at higher speeds. The average speed increases by 70 percent at the 100-percent CACC market penetration rate compared to the baseline strategy (i.e., 0-percent CACC market penetration)
  • Increasing the number of vehicles with VAD allowed CACC vehicles to locate string leaders more easily at market penetration rates between 20 and 40 percent in which the average speed increased by 8 percent and fuel efficiency remained nearly constant.
  • The fuel efficiency first increased as CACC market penetration increased but decreased as the CACC market penetration rate reached 40 percent or higher. The highest average fuel efficiency was observed under the 30-percent CACC market penetration rate, at which mpg reached 26, which was 5 percent higher than the baseline strategy. The CACC string operation improved fuel efficiency in the low to medium CACC market penetration scenarios because it mitigated traffic congestion.
  • The corridor allowed about 30 percent more traffic to enter the network with no reduction in travel time with 100-percent CACC market penetration. However, improving isolated bottlenecks did not necessarily bring about a benefit of the same magnitude for the entire corridor

Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California

Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California
Source Publication Date
03/01/2021
Author
Liu, Hao; Xiao-Yun Lu; Steven E. Shladover; and Zhitong Huang
Publisher
USDOT Federal Highway Administration
Other Reference Number
FHWA-HRT-21-039
Results Type