A Cooperative Truck Platooning Approach on Ramp Merging Area of Highway in Mixed Traffic

Truck platooning has emerged as a promising solution for improving traffic efficiency on highways. This paper proposes an optimization model for connected automated trucks (CATs) that addresses the trajectory optimization problem for CATs as human-driven trucks (HDTs) merge onto highways from on-ramps in a mixed traffic scenario. To maximize the speed of the mixed traffic system, we propose a mixed integer programming (MIP) model based on a rolling horizon algorithm. The algorithm optimizes CATs’ trajectories iteratively, taking into account real-time updates on their driving state, and guides HDTs to follow the trajectories of CATs. The numerical experiments demonstrate that the increasing number of HDTs on the on-ramp negatively impacts the overall system performance, leading to higher traffic delay and reduced system stability. Furthermore, accounting for the stochastic behavior of HDTs can lead to increased instability in the system. Finally, we show that the objective function that comprehensively considers the system speed and platooning time effectively minimizes the delay of all vehicles.