An improved transformer-based model for long-term 4D trajectory prediction in civil aviation

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-06-24 DOI:10.1049/itr2.12530
Aofeng Luo, Yuxing Luo, Hong Liu, Wenchao Du, Xiping Wu, Hu Chen, Hongyu Yang
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Abstract

Four-dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short-term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long-term prediction due to the iterative output that accumulates error. To address this issue, a transformer-based long-term trajectory prediction model is proposed here, which utilizes the self-attention mechanism to extract time series features from historical trajectory data. For long-term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one-step inference strategy.

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基于变压器的民用航空长期 4D 轨迹预测改进模型
四维轨迹预测是空中交通管理的重要组成部分,其准确性与航空运输的效率和安全密切相关。尽管长短期记忆(LSTM)或其变体已在近期研究中得到广泛应用,但由于其迭代输出会积累误差,因此在长期预测中可能会产生不可接受的结果。为解决这一问题,本文提出了一种基于变压器的长期轨迹预测模型,该模型利用自注意机制从历史轨迹数据中提取时间序列特征。针对长期预测场景,我们引入了轨迹稳定模块,以确保时间序列的静态性,从而获得更好的可预测性。此外,我们还改进了变压器输出策略,通过单步生成预测序列,而不是串行动态解码,从而有效提高了预测精度和推理速度。利用从中国西南空中交通管理局获得的真实数据对所提出的模型进行了验证。实验结果表明,该模型优于基准模型。进一步的消融实验和可视化实验分析了轨迹稳定和一步推理策略的影响。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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