基于多类排队网络的多机场系统旅行时间预测

Kailin Chen, Shaoyu Wang, Jianfeng Mao
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引用次数: 1

摘要

本文通过对多机场系统中的飞机进行多类排队网络的建模和仿真,考虑多机场系统中飞机的飞行时间预测,该网络可以系统地捕捉多个机场和终端空域之间的复杂耦合关系,以及不同交通流模式下飞行轨迹的复杂性。在该多类别排队网络模型中,每一类排队网络被命名为一类顾客,并使用一种交通流模式数据进行建模,该交通流模式被识别为一组飞行轨迹。相应地,机场和空域被建模为具有非同构和时变到达率、服务率和服务器容量的网络服务器,以按照特定的路由概率为这些类别的客户提供服务。然后,利用历史四维飞行轨迹数据,可以合理估计建立多类排队网络模型所需的所有参数。为了说明该模型的优越性,通过对多等级排队网络的模拟,预测了每一类顾客(即遵循特定流模式的飞机)的平均旅行时间和单个航班的到达时间,并将其与实际旅行时间进行了比较。以中国粤港澳大湾区为典型的多机场系统为例,展示了所提出的多类排队网络仿真模型的预测性能。实例仿真实验表明,所提模型能很好地拟合多机场系统。在大多数时间段内,模拟平均行程时间与实际平均行程时间的百分比误差小于5%。随机单个航班的行程时间预测在点估计方面可以达到1%左右的百分比误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Travel Time Prediction for Multi-Airport Systems Via Multiclass Queuing Networks
In this paper, we consider predicting travel time for aircraft operated in multi-airport systems by modeling and simulating a multiclass queuing network, which can systematically capture the complicated coupling relationship among multiple airports and terminal airspace and the complex nature of flight trajectories following different traffic flow patterns. In this multiclass queuing network model, each class of queuing network, named a class of customers, is modeled with the data of a traffic flow pattern, which is identified for a cluster of flight trajectories. Airports and airspace sectors are correspondingly modeled as networked servers with nonhomogeneous and time-varying arrival rate, service rate and server capacity to serve those classes of customers following their specific routing probabilities. Then, all of the parameters for setting up the multiclass queuing network model can be properly estimated using historical 4D flight trajectory data. To illustrate the superiority of this model, both average travel time for each class of customers, i.e., aircraft following a particular flow pattern, and the arrival time for an individual flight are predicted via simulations of a multiclass queuing network, and furthermore, compared with the real travel time. A typical example of a multi-airport system, the Guangdong-Hong Kong-Macau Greater Bay Area in China, is utilized to showcase the prediction performance of the proposed multiclass queuing network simulation model. The simulation experiments of the case study demonstrate that the proposed model well fits this multi-airports system. For most of the time periods, the percentage error (PE) of simulated average travel time and real average travel time is less than 5%. The travel time prediction for a random individual flight can achieve around 1% of the percentage error in terms of point estimation.
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