Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

Abstract

Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.