4D Trajectory Lightweight Prediction Algorithm Based on Knowledge Distillation Technique

Weizhen TangJie Dai^*Zhousheng HuangBoyang HaoWeizheng Xie

• Civil Aviation Flight University of China, Guanghan, China

To address the shortcomings of current 4D trajectory prediction—namely limited multi‐factor feature extraction and excessive computational cost—this paper proposes a lightweight 4D trajectory forecasting algorithm based on RCBAM–TCN–LSTM knowledge distillation. Our approach innovatively combines the Residual Convolutional Block Attention Module (RCBAM) for powerful spatial feature extraction with the Temporal Convolutional Network–LSTM (TCN–LSTM) for efficient temporal modeling, and employs a teacher–student distillation framework to achieve both model compression and performance enhancement. We use historical ADS B trajectory data from Zhuhai Jinwan Airport, preprocessing raw trajectories via cubic spline interpolation and a uniform‐step sliding window. The teacher network leverages residual structures and channel‐spatial attention in RCBAM to deeply extract high‐dimensional spatiotemporal features, while the student network integrates dilated causal convolutions and a two‐layer LSTM within a TCN–LSTM architecture. In the distillation phase, RCBAM’s predictions serve as soft labels and actual observations as hard labels, jointly guiding student training so that it inherits the teacher’s distribution knowledge while preserving precise real‐world signals. Experimental results in multi‐step prediction scenarios show that the distilled RCBAM–TCN–LSTM model reduces MAE, RMSE, and MAPE by an average of 40%–60% compared to the original RCBAM and TCN–LSTM, and improves R² by 4%–6%. Crucially, this method achieves significant model lightweighting and real‐time deployability, enabling efficient online air‐traffic monitoring and early warning on standard CPUs and embedded devices.