A Dynamic Adaptive Algorithm for Merging Into Platoons in Connected Automated Environments

The automated merging control is one of the connected and automated vehicle applications that is expected to improve the operation and safety of freeway merging areas. This study introduces a dynamic adaptive algorithm for automated merging control based on a cost function of travel time for both on-ramp and mainline vehicles. The developed algorithm is designed to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp and with minimal disruption to the mainline traffic. The algorithm was evaluated under different mainline traffic demands, ranging from light to heavy conditions, using a simulation model for a one-mile freeway segment with two lanes and a single-lane on-ramp. Platooning is implemented in the simulation based on Cooperative Adaptive Cruise Control. The performance of the algorithm is compared to a base case where mainline vehicles cooperatively decelerate to help on-ramp vehicles merge. The results show that the proposed algorithm reduced ramp delays in the range of 38% to 91% for different traffic conditions, without disrupting mainline operation. Additionally, travel time reliability index improved in the range of 18% to 48% under different traffic conditions. According to the simulation results, the proposed automated merging algorithm is henceforth considered reliable, with its promising performance.