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

Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment