A scalable macro–micro approach for cooperative platoon merging in mixed traffic flows

Abstract

This study presents a new approach to efficiently coordinate platoon movements in mixed traffic environments. The approach synergises macroscopic, network-wide traffic control models with microscopic, individual vehicle dynamics. This overcomes the limitations of using either strategy in isolation: macroscopic models lack detailed vehicle interactions, while microscopic models do not scale. By combining both strategies, the framework provides a scalable solution for mixed traffic networks.

At the macroscopic level, the framework dynamically optimises speed limits and ramp metering rates to reduce total travel time and queue lengths, establishing a high-level control mechanism that facilitates microscopic implementations. At the microscopic level, the strategy focuses on the trajectory planning of automated vehicles (AVs). Vehicles are organised into platoons with an AV leader, enabling cooperative behaviour in mixed traffic. The number of platoons and their entry into the merging area are carefully regulated to match macroscopic control references. In addition, we propose a novel passing sequence rule for platoons in the merge area. This is further supported by a virtual platooning method for trajectory planning of AVs.

The effectiveness of the integrated approach is demonstrated through rigorous microscopic simulations. Our method reduces the total travel time by more than 30% with a 20% AV penetration rate compared to the no-control scenario. It also outperforms the existing macroscopic approach even at a 10% AV penetration rate. Furthermore, it balances queue lengths across multiple merging areas. Our integrated control strategy facilitates the integration of AVs into existing transport systems, resulting in a more efficient, coordinated and adaptable system.