Our latest research paper, “A deep multi-agent reinforcement learning framework for climate-aware aircraft trajectory planning considering air traffic complexity,” is now published in Knowledge-Based Systems.
In this work, Fateme Baneshi proposes a framework to reconcile climate-optimal flight trajectories with the operational requirements of the ATM system. First, individual aircraft trajectories are optimized to mitigate climate impact. Then, a distributed multi-agent reinforcement learning (MARL) approach is introduced to compensate for the adverse effects these trajectories may impose on the ATM system manageability by reducing air traffic complexity.
The results show that this method can balance climate impact, operating costs, and air traffic complexity, offering a promising pathway toward more sustainable and operationally feasible flight planning.
A deep multi-agent reinforcement learning framework for climate-aware aircraft trajectory planning considering air traffic complexity. Fateme Baneshi, María Cerezo-Magaña, Manuel Soler. Knowledge-Based Systems, Volume 332, 2026, 114826 https://doi.org/10.1016/j.knosys.2025.114826
Abstract
The aviation sector is facing increasing pressure to reduce its environmental footprint, necessitating effective and immediate solutions. Flight planning has emerged as a short-term measure to mitigate aviation-induced climate effects. However, existing literature has primarily focused on individual trajectory optimization, overlooking interactions between flights and their implications for the air traffic management (ATM) system. This study addresses this gap by introducing a two-stage framework to reconcile climate-optimal flight trajectories with the operational requirements of the ATM system. First, individual aircraft trajectories are optimized to mitigate their climate impact. Subsequently, a distributed multi-agent reinforcement learning (MARL) method is proposed to compensate for the adverse effects these trajectories may impose on the ATM system manageability by mitigating air traffic complexity. The proposed approach leverages the multi-agent proximal policy optimization algorithm and adapts it to address the scalability issue in real traffic scenarios. The effectiveness of the proposed framework is validated through a case study using real traffic data over European airspace. Experimental results show that the proposed approach balances environmental objectives with air traffic manageability, achieving up to 24 % mitigation in climate impact while maintaining air traffic complexity at levels comparable to standard business-as-usual trajectories, with a 1.8 % increase in operational costs.