  Given the aging infrastructure and the anticipated growing number of highway
work zones in the United States, it is important to investigate work zone merge
control, which is critical for improving work zone safety and capacity. This
paper proposes and evaluates a novel highway work zone merge control strategy
based on cooperative driving behavior enabled by artificial intelligence. The
proposed method assumes that all vehicles are fully automated, connected and
cooperative. It inserts two metering zones in the open lane to make space for
merging vehicles in the closed lane. In addition, each vehicle in the closed
lane learns how to optimally adjust its longitudinal position to find a safe
gap in the open lane using an off-policy soft actor critic (SAC) reinforcement
learning (RL) algorithm, considering the traffic conditions in its surrounding.
The learning results are captured in convolutional neural networks and used to
control individual vehicles in the testing phase. By adding the metering zones
and taking the locations, speeds, and accelerations of surrounding vehicles
into account, cooperation among vehicles is implicitly considered. This
RL-based model is trained and evaluated using a microscopic traffic simulator.
The results show that this cooperative RL-based merge control significantly
outperforms popular strategies such as late merge and early merge in terms of
both mobility and safety measures.
