4D trajectory lightweight prediction algorithm based on knowledge distillation technique.

Abstract

Introduction: To address the challenges of current 4D trajectory prediction-specifically, limited multi-factor feature extraction and excessive computational cost-this study develops a lightweight prediction framework tailored for real-time air-traffic management.

Methods: We propose a hybrid RCBAM-TCN-LSTM architecture enhanced with a teacher-student knowledge distillation mechanism. The Residual Convolutional Block Attention Module (RCBAM) serves as the teacher network to extract high-dimensional spatial features via residual structures and channel-spatial attention. The student network adopts a Temporal Convolutional Network-LSTM (TCN-LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. Historical ADS-B trajectory data from Zhuhai Jinwan Airport are preprocessed using cubic spline interpolation and a uniform-step sliding window to ensure data alignment and temporal consistency. In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student training.

Results: In multi-step prediction experiments, the distilled RCBAM-TCN-LSTM model achieved average reductions of 40%-60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN-LSTM models, while improving R ^² by 4%-6%. The approach maintained high accuracy across different prediction horizons while reducing computational complexity.

Discussion: The proposed method effectively balances high-precision modeling of spatiotemporal dependencies with lightweight deployment requirements, enabling real-time air-traffic monitoring and early warning on standard CPUs and embedded devices. This framework offers a scalable solution for enhancing the operational safety and efficiency of modern air-traffic control systems.