准确的航迹预测在未来空中交通的可持续管理中扮演重要角色。为提高长时航迹预测精度，从机载快速存储记录器（quick access recorder, QAR）航迹数据出发，提出了考虑管制规则的分维多步航迹预测方法。首先根据飞机运行特性对航迹特征进行分类及针对性变换；然后基于分维特征选取不同组合的影响因素作为特征矩阵，并考虑实际管制规则，提出分阶段采样；最后利用长短时记忆（long short-term memory, LSTM）神经网络建立航迹预测模型来探究飞机位置变化和前序航迹点数据特征之间的关系。使用671条城市对航迹数据进行仿真实验，通过结果分析和泛化分析验证了所提模型在有效性、鲁棒性及可移植性方面的优势，研究成果为管制工作提供了参考。

Accurate trajectory prediction plays a vital role in the sustainable management of future air traffic. To enhance the accuracy of long-term trajectory prediction, a multi-step trajectory prediction method considering regulatory rules was proposed based on airborne quick access recorder (QAR) trajectory data. Firstly, trajectory features were classified and transformed according to the aircraft's operational characteristics. Then, different combinations of influencing factors were selected as the feature matrix based on the fractal dimension feature, and phased sampling was proposed considering the actual air traffic management rules. Finally, a long short-term memory (LSTM) neural network was employed to establish a trajectory prediction model to explore the relationship between aircraft position changes and preceding trajectory point data features. 671 urban-pair trajectory data was used for simulation experiments, and the advantages of the proposed model in terms of effectiveness, robustness, and portability were verified through result analysis and generalization analysis. The research results provide a valuable reference for air traffic management work.