Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems

Lechen Wang, Xuechun Li, Jianfeng Mao
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引用次数: 3

Abstract

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao’an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight’s destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal features we designed when the flight’s entering MAS time is given. For TMA travel time prediction, a case study of Guangdong-Hong Kong-Macao Greater Bay Area (GHM-GBA), a typical MAS which contains five major airports closely located within 120km, is carried out using actual historical 4D trajectory data. Finally, Using two months 4D trajectories data, PEK to ZGSZ, the result exhibits the best accuracy, a measurement we define for prediction, of the longterm prediction of ETA given departure time is 92%, and mean absolute error (MAE) is 6.09 minutes.
基于ARIMA和双向LSTM的多机场系统预计到达时间预测
航空运输系统的交通状态预测是一个具有挑战性的问题,由于与城市交通系统相比,它受到许多高度相关的因素和更复杂的交通管理方案的影响,因此尚未得到充分的探索。当面对多机场系统(MAS)时,这一任务变得更加艰巨,因为几个主要机场通过有限的终端空域紧密相连。在这项工作中,我们提出了一种使用时间序列模型和递归神经网络来估计航班到达MAS的时间(ETA)的新方法,该方法可以潜在地用于航班延误预测和拥堵分析。实验利用北京首都国际机场(PEK)到深圳宝安国际机场(ZGSZ)两个月的四维轨迹数据。将整个预测工作分解为两个子问题,即从航班出发地到MAS入口处的航路旅行时间预测,定义为距离MAS机场300km的位置,以及从航班入口处到目的地的终端机动区域(TMA)旅行时间预测。采用时间序列预测模型——自回归综合移动平均(ARIMA),在给定航班出发时间的条件下,对航路中旅行时间进行预测。双向长短期记忆(LSTM)是一种递归神经网络,利用时空特征来预测到达路径中的旅行时间。为了设计输入特征,我们利用基于密度的空间聚类(DBSCAN)方法,结合Voronoi图,从在MAS中运行的每架飞机的历史飞行轨迹中提取空间信息,然后选择观测时间窗口来捕获每次飞行的时间信息。在给定航班进入MAS时间的情况下,利用设计的时空特征,构建多元堆叠全连通-双向LSTM (MSFCB-LSTM)模型进行短期预测。在TMA旅行时间预测方面,以粤港澳大湾区(GHM-GBA)为例,利用实际历史4D轨迹数据进行了TMA旅行时间预测。最后,利用两个月的四维轨迹数据,PEK到ZGSZ,结果表明,在给定出发时间的情况下,ETA的长期预测精度为92%,平均绝对误差(MAE)为6.09分钟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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