A simulation-based approach for optimizing the placement of dedicated lanes for autonomous vehicles in large-scale networks

This study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)-dedicated lane implementation at large-scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi-level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV-dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human-driven vehicles to a fully AV environment.