Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments.

Highlights • A novel traffic management strategy in automated driving environments is proposed. • A method to adjust the driving aggressiveness of automated vehicles (AV) is developed. • The proposed methodology is to maximize the performance of mixed traffic conditions. • VISSIM simulations are conducted to investigate the feasibility of the proposed methodology. Abstract The advancement of vehicular technologies for automated driving will lead to a mixed traffic flow that depends on the interaction between automated vehicles (AVs) and manually driven vehicles (MVs) because the market penetration rate (MPR) of AVs will gradually increase over time. Because automated driving environments provide us with a valuable opportunity for controlling individual vehicle operation, a strategic policy for managing AVs operation is expected to enhance the performance of the traffic stream. Therefore, the operation of AVs needs to be properly determined to cope with various traffic and road conditions and thereby facilitate smooth and effective vehicle interactions. This study proposed a novel traffic management strategy in automated driving environments by adjusting the driving aggressiveness of AV operation, defined as automated driving aggressiveness (AuDA). VISSIM microscopic simulation experiments were conducted to derive the proper AuDAs to enhance both the traffic safety and the mobility performance. Traffic conflict rates and average travel speeds were used as indicators for the performance of safety and operations. While conducting the simulations, the level of service (LOS) and MPR of the AVs were also considered. In addition, the relationship between key variables for adaptive cruise control (ACC) operations and AuDA policies was explored to better support the understanding of how the proposed methodology works in practice. Promising results showed that the proposed methodology would be effective in optimizing the performance of mixed traffic conditions. Furthermore, the outcome will be valuable in developing various policies and guidelines to manage the operation of AV in automated driving environments. [ABSTRACT FROM AUTHOR]