Graph Neural Networks for Aerodynamic Analysis of Truck Platoon Under Heterogeneous Traffic Environment

Recent advancements in connected vehicle technology have the potential to transform the efficiency and sustainability of transportation systems. Among these, truck platooning has emerged as a promising strategy to enhance freight delivery operations. However, the generalization of truck platoon modeling and the economic impacts of implementing such systems remain areas that require deeper exploration. This paper introduces a novel graph neural network-based surrogate model, namely Graph Network-based Platoon Simulator (GNPS), designed to predict aerodynamic drag forces in truck platoons across a wide range of traffic and environmental conditions. By representing the platoon and surrounding traffic as a heterogeneous graph, the proposed GNPS model leverages node-level features such as wind configurations and vehicle geometries, alongside edge-level features like inter-vehicle distances. In comparison to traditional CFD simulations, the proposed GNPS model provides a cost-effective and computationally efficient alternative, offering dynamic adaptability to real-world traffic scenarios.