Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

OPTIMAL FLOW CONTROL PROBLEM IN AIR TRAFFIC CONTROL:PARTⅠ

The fundamental case is considered in which flights from many destinations must be scheduled for arrival at a single congested airport having limited capacities.An air traffic control(ATC)model is developed in this case.A new and efficient algorithm for the optimal solution of ground holding strategy problem(GHSP)is put forward and verified by a numerical example.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

基于关联规则挖掘的终端区交通拥挤传播规律研究

本文基于空中交通流运行现象建立交通拥挤传播规则挖掘模型,从数理角度分析并表征终端区交通流拥挤状态的传播现象。首先,对终端区交通流按照航段结构进行划分并给出交通状态评价指标,采用模糊C均值算法对终端区进场交通流状态进行聚类划分;其次,使用OApriori算法挖掘终端区进场交通流拥挤传播规则;最后,基于实测历史数据构建北京终端区全空域及机场模型(TAAM,total airspace and airport modeler)仿真场景,模拟终端区在不同流量分布特征下的运行情况并进行分析,结果表明:本文模型分析所得的传播规律与实际情况相符,并发现优化终端区进场点流量分布能显著减少终端区拥挤状态的传播现象。

基于复杂网络的空中交通流量短期预测

为合理预测空中交通流量,结合复杂网络链路预测进行研究。首先,将时间序列转化为可视图得到拓扑特征量,然后结合基于局部信息、路径和随机游走的算法,比较在三亚不同扇区内的预测精度,发现RWR0.85算法预测精度最高。由于链路预测只能预测可能存在的连边,不能预测节点,因此引入D⁃S证据理论预测流量值,预测精度最高可达99.85%。结果表明,复杂网络链路预测结合D⁃S证据理论进行空中交通流量的预测是可行有效的,为进一步深入研究奠定了基础。

机场航班时刻资源管理研究进展

繁忙机场都会面临严重的交通拥堵和航班延误问题。解决该问题的一个有效手段是对机场航班时刻资源进行优化管理。全面介绍了航班时刻资源管理相关的研究进展,系统回顾了机场公布容量设置、机场航班时刻配置的主要方法和手段。从公布容量确定、单机场、机场网络层面和机场群航班时刻配置及航班时刻配置技术复杂性等方面,得出机场航班时刻资源优化配置的关键技术问题。展望了机场航班时刻资源管理的主要方向,并对下一步研究提出建议。

基于实时交通流的上海市机动车碳污同源排放清单

为满足交通碳污减排政策多样化、差别化和精细化发展需求,扩大交通排放测算范围、提高测算准确度和时空分辨力,提出了一种基于实时交通流的高时空分辨力机动车碳污同源排放清单构建方法,综合交通模型、IVE模型和相关算法建立了覆盖上海全市的机动车碳污同源排放清单。结果显示,二氧化碳、氮氧化物周排放规律相近,呈现出明显的周末低工作日高的特征;二氧化碳分时变化规律呈现明显的双峰,而氮氧化物受重型车影响,峰值出现在夜间;由于各车型的碳污分担率不同且活动区域不同造成了上海市机动车二氧化碳和氮氧化物排放的空间分布差异明显;碳污同源排放清单中,NOx、PM、CO、VOCs、CO_(2)的月均排放量分别为5160t、440t、1.7×10^(4)t、732t和2.46×10^(6)t;排放分担率表明,NOx、PM等污染物主要来自重货车及大货车排放;CO、VOCs和CO_(2)排放来源以小客车排放占比最高。

基于GWO-HMM的空中交通网络流系统态势预测研究

针对空中交通流量管理部门如何更高效地实施流量管理的问题,本文将态势感知理论应用于空中交通网络流系统(ATNFS,air traffic network flow system),建立空中交通网络流系统的运行态势预测模型。首先,给出了空中交通网络流系统的态势感知过程,从节点和航线的角度筛选出航线饱和度、不正常航班率、节点饱和度、节点延误架次比、节点航班取消率5个态势要素,使用态势值作为态势理解的指标;其次,分析隐马尔可夫模型(HMM,hidden Markov model)的优势与不足,建立了基于灰狼优化(GWO,grey wolf optimization)算法和改进隐马尔可夫模型的态势预测模型;最后,使用某空中交通网络流系统的实际运行数据进行算例验证。结果表明,改进后的预测模型相较于原本的隐马尔可夫预测模型精度更高,预测结果更准确。

基于任务优先权的军民航飞行活动协同排序模型

随着空域资源需求的不断增大,军民航间飞行矛盾日益突显。为解决此问题,本文以国务院、中央军事委员会空中交通管制委员会提出的“军民航空管联合运行”为背景,引入军民航共享空域的概念,重点研究了在此类空域中军民航飞行活动协同排序(CMFCS,civil-military aviation flight activity collaborative sequencing)问题。首先,基于军民航各自飞行任务特点与差异,对军民航飞行任务的种类进行划分,并使用层次分析法确定各类飞行任务的优先权原则;其次,以军民航飞行活动总延误时间成本最小为目标,建立CMFCS模型;最后,使用遗传算法对模型进行求解,确定军民航飞行活动批准进入共享空域的时间序列。研究结果表明,与经典的先到先服务(FCFS,first come first service)策略相比,协同排序策略得到的总延误时间成本降低了72.17%,优化效果显著且更符合实际,能够实现军民航共同使用国家空域资源,保障飞行活动安全、有序、高效地运行。

增强鲁棒性的点融合系统进场排序与调度

现有进场航班排序与调度模型较少考虑不确定因素的影响,本文基于点融合系统(PMS,point merge system)连续下降进近时间符合高斯分布的特点,建立增强鲁棒性的两阶段进场排序与调度模型。第一阶段,描述随机变量带来的不确定性,基于机会约束规划理论设计带有额外缓冲区的静态排序与调度模型,以飞行时间和次序变更量为指标确定最佳额外缓冲区;第二阶段,补偿不可预知原因导致飞行时间超出缓冲区的情况,采用基于滑动时间窗的启发式算法对第一阶段的方案进行动态调整。通过Monte-Carlo模拟不确定性场景下的连续下降进近时间,仿真结果表明,在高、中、低3种交通密度下,所提出的两阶段模型相较于其他模型在鲁棒性和公平性的平衡方面表现出了良好性能。

基于复杂网络表征ATF时间序列动力学特性

针对空中交通流仿真、预测与控制工作中普遍需要空中交通流时空动态基本特征的问题,应用复杂网络方法从实测监视数据中挖掘空中交通流的内在特性。探索了运用复杂网络对空中交通流时间序列的非线性特性进行分析,通过网络度分布的分析验证了航路交通流具有混沌和分形特性。此外还发现交通流HVG转换算法网络度分布近似符合指数函数p(k)~exp(-γk)且混沌的强弱程度与参数值γ大小存在正相关关系,验证了交通流VG转换算法网络度分布幂指数λ与Hurst指数的线性关系。基于复杂网络理论的研究工作从全新的视角审视了空中交通流,丰富了刻画时间序列非线性动力特性的手段。

AUCTIONING METHOD FOR AIRSPACE CONGESTING RESOURCE ALLOCATION AND GAME EQUILIBRIUM ANALYSIS

The airspace congestion is becoming more and more severe.Although there are traffic flow management（TFM）initiatives based on CDM widely applied,how to reschedule these disrupted flights of different airlines integrating TFM initiatives and allocate the limited airspace resources to these airlines equitably and efficiently is still a problem.The air traffic management（ATM）authority aims to minimizing the systemic costs of congested airspaces.And the airlines are self-interested and profit-oriented.Being incorporated into the collaborative decision making（CDM）process,the airlines can influence the rescheduling decisions to profit themselves.The airlines maybe hide the flight information that is disadvantageous to them,but is necessary to the optimal system decision.To realize the coincidence goal between the ATM authority and airlines for the efficient,and equitable allocation of airspace resources,this paper provides an auction-based market method to solve the congestion airspace problem under the pre-tactic and tactic stage of air traffic flow management.Through a simulation experiment,the rationing results show that the auction method can decrease the total delay costs of flights in the congested airspace compared with both the first schedule first service（FSFS）tactic and the ration by schedule（RBS）tactic.Finally,the analysis results indicate that if reallocate the charges from the auction to the airlines according to the proportion of their disrupted flights,the auction mechanism can allocate the airspace resource in economy equitably and decrease the delay losses of the airlines compared with the results of the FSFS tactic.

“空中大通道”航空流网络结构化解析——以京昆、广兰和沪兰大通道为例

拟搭建一个航空流网络结构化解析的方法论框架并应用于京昆、广兰和沪兰大通道,旨在查找航空流运行关键制约因素。研究发现:①“空中大通道”途中航空流延误吸收明显,这主要源于飞行路径分化和航空流调整。②“空中大通道”建设使以往共享航段/活动范围设置发生较大改变,空中过流能力得到显著加强,途中航空流已具有灵活空域使用特性。但是不同大通道整体航空流运行性能也呈现个性化差异。③广兰大通道具有最为典型的空域均衡使用特征,京昆大通道日内高密度空域位置及范围较为固定而广兰和沪兰大通道则分别呈现范围多变和范围递减特征,在有限空域容量条件下即造成拥堵时段和持续时间的差异,航空流参与量及其稳定性、上/下行飞行路径汇聚等在其中共同发挥重要作用。

空中航路点通行能力评估与优化方法

航路作为空中交通流的承载体,其瓶颈主要在于交叉航路节点的通行能力。为提高航路网络的运行安全和效率,优化航路网络的拓扑结构,本文提出基于仿真样本-代理模型-变量优化的航路点通行能力优化框架。根据历史数据统计繁忙航路点的高峰时段航班数量及繁忙高度层流量占比情况,提出基于仿真的通用航路点通行能力评估方法;以航路点构型中的航路输入角度、流量占比和输出角度作为输入特征,将航路点通行能力评估方法得到的通行能力值作为样本标签,训练多层感知机代理模型,实现对航路点通行能力评估的快速计算;基于粒子群算法,以调整航路角度和交通流分布作为优化手段,对航路点通行能力进行优化。实验结果表明,构建的航路点通行能力评估方法能较好地反映实际航路点的通行能力状况,使用多层感知机作为代理模型能够满足航路点通行能力预测代理任务的精度要求,平均绝对误差为1.19,优化的航路点通行能力相比优化前提升了37.9%,验证了优化框架的有效性。

考虑自稳定性的异质航空器跟驰行为模型构建

面向空中交通流场景中异质航空器运行安全及稳定性的需求,提出了一种全新的异质航空器跟驰行为模型,促使异质交通流更加安全和稳定地运行。首先,考虑了空中交通流中同向飞行的前后航空器之间的间距变化,以及后机自身当前与历史时间的速度差,更好地描述了跟驰行为中的非线性运动现象;然后,采用稳定性分析方法对异质航空器跟驰模型的稳定性进行了推导和分析,得出了初始纵向飞行间隔与后机反应敏感度之间的关系;最后,验证结果表明,该模型能够使后机更快地达到新的目标速度,与前机形成新的稳定跟驰状态,能够有效地缓解交通拥堵,具有广阔应用前景。

基于多尺度时空信息的空中交通流智能预测技术

为解决空中交通流预测中的时空复杂性问题,提出了一种基于多尺度时空视角的空中交通流智能预测方法。针对空中交通网络中的非规则空间关系和超长时间序列中的周期性和趋势性特征进行了建模,通过构建图卷积层和堆叠的扩张因果卷积层,捕捉空中交通流的多维关联性,并提供对未来时刻交通流的精准预测。试验结果表明,该方法在多个航空网络数据集上的预测精度高于传统方法,具有实际应用价值,可为空中交通管理管系统智能决策提供参考。

基于VOSviewer和CiteSpace的空中交通流量管理研究综述

以中国知网(CNKI)和Web of Science (WOS)数据库内的文献作为数据来源,以2013—2023年为检索年限,将CNKI中以“空中交通流量管理”为主题的956篇文献和WOS中以“air traffic flow management”为主题的379篇文献作为研究对象,使用VOSviewer和CiteSpace文献计量工具生成了作者合作网络图,研究机构共现图、关键词共现图以及关键词突变图,以此来了解并分析有关空中交通流量管理的研究内容。结果表明,对空中交通流量管理的研究关注度较高,发文量总体呈现上升趋势,研究机构大多集中在高校。关键词研究显示,空中交通流量管理研究的方向包括流量管理、深度学习等方面。结论为空中交通流量管理研究提供理论依据。

基于集成学习的航空器滑出时间预测研究

为提升航空器离场滑出时间的预测精度,分析了影响滑出时间的各类因素,引入场面运行状况和气象条件两类特征,基于装袋方法、随机森林、自适应增强和梯度提升等四种典型的集成学习方法,构建了滑行时间预测模型。以美国肯尼迪机场为算例,采用判决系数、RMSE和MAE等性能度量指标验证算法预测性能。实验结果表明:气象特征的引入能够提升滑出时间预测精度;与其它回归算法对比,集成学习的预测误差较小;分析集成方法下的学习曲线发现自适应增强和梯度提升方法能够有效避免过拟合现象。研究结果可用于集成化场面管理软件的开发应用。