China’s corridors-in-the-sky design and space-time congestion identification and the influence of air routes’ traffic flow

With rapid development of air transportation,the airspace structure of the future will need to be flexible and dynamic to accommodate the increase in traffic demand.The corridors-in-the-sky has become a new technology to support the full exploitation and utilization of airspace resources.This paper proposes a method of designing corridor,identifying congestion state,and analyzing the influence of air routes’traffic flow.From this,we have reached a number of conclusions.(1)The congestion periods present the multi-peak"wavy"scattered distributions and the peaks back-end agglomeration characteristics in the whole day.(2)The congestion segments present the structural characteristics of unbalanced coverage and concentrated distribution to the crossing points.The corridors with high congestion level present as an italic"N-shaped"frame,which presents incomplete penetration of short segments.(3)For the temporal and spatial interaction,there are two types of congestion segments,and there are some common congestion periods in different congestion segments of multiple corridors.The high-density air route plays a relatively decisive role in corridor congestion,and the influence of two directions is unbalanced.This research can provide a basis for the dynamic evaluation of China’s airspace resources and corridors construction in the future.

Air Route Network Generation Based on Traffic Assignment

Air route network is the carrier of air traffic flow,and traffic assignment is a method to verify the rationality of air route network structure.Therefore,air route network generation based on traffic assignment has been becoming the research focus of airspace programming technology.Based on link prediction technology and optimization theory,a bi-level programming model is established in the paper.The model includes an upper level of air route network generation model and a lower level of traffic assignment model.The air route network structure generation incorporates network topology generation algorithm based on link prediction technology and optimal path search algorithm based on preference,and the traffic assignment adopts NSGA-Ⅲalgorithm.Based on the Python platform NetworkX complex network analysis library,a network of 57 airports,383 nodes,and 635 segments within China Airspace Beijing and Shanghai Flight Information Regions and 187975 sorties of traffic are used to simulate the bilevel model.Compared with the existing air route network,the proposed air route network can decrease the cost by 50.624%,lower the flight conflict coefficient by 33.564%,and reduce dynamic non-linear coefficient by 7.830%.

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.

GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

With continuous urbanization,cities are undergoing a sharp expansion within the regional space.Due to the high cost,the prediction of regional traffic flow is more difficult to extend to entire urban areas.To address this challenging problem,we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet,which predicts traffic flow of surrounding areas based on inflow and outflow information in central area.The method is data-driven,and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix.We introduce adversarial training to improve performance of prediction and enhance the robustness.The generator mainly consists of two parts:abstract traffic feature extraction in the central region and traffic prediction in the extended region.In particular,the feature extraction part captures nonlinear spatial dependence using gated convolution,and replaces the maximum pooling operation with dynamic routing,finally aggregates multidimensional information in capsule form.The effectiveness of the method is evaluated using traffic flow datasets for two real traffic networks:Beijing and New York.Experiments on highly challenging datasets show that our method performs well for this task.

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.

An Improved FN Algorithm for Community Division of Air Route Network

Community division is an important method to study the characteristics of complex networks.The widely used fast-Newman(FN)algorithm only considers the topology division of the network at the static layer,and dynamic traffic flow demand is ignored.The result of the division is only structurally optimal.To improve the accuracy of community division,based on the static topology of air route network,the concept of network traffic contribution degree is put forward.The concept of operational research is introduced to optimize the network adjacency matrix to form an improved community division algorithm.The air route network in East China is selected as the object of algorithm comparison experiment,including 352 waypoints and 928 segments.The results show that the improved algorithm has a more ideal effect on the division of the community structure.The proportion of the number of nodes included in the large community has increased by 21.3%,and the modularity value has increased from 0.756 to 0.806,in which the modularity value is in the range of[-0.5,1).The research results can provide theoretical and technical support for the optimization of flight schedules and the rational use of air route resources.

空中交通CPS结构特性及韧性评估

针对空中交通体系结构复杂、耦合性强、脆弱性高的特点,为缓解因受扰导致的级联失效现象,对其结构韧性进行研究。建立了空中交通信息物理系统(CPS)模型,提出同层度与层间度、同层介数与层间介数等指标,分析级联失效过程;提出空中交通CPS韧性的概念,并采用定量评估方法对受损能力和恢复能力进行度量;对比不同扰动-恢复策略下的空中交通CPS表现,制定最佳恢复策略,提高受扰时的韧性。以华东地区空中交通CPS为例进行分析,结果表明:空中交通CPS网络度分布服从幂率分布,介数服从指数分布;在基于介数度值扰动下,采用介数恢复策略可以有效提高受扰时空中交通CPS的韧性。

远郊大型冬季活动客流演进及路径分流研究

有效的客流组织是保证大型活动成功举办的关键,通过大型冬季活动的客流特征及意向调查,建立了观众路径选择模型和客流动态演进模型,分析了大型活动各设施节点的客流分布和客流排队时间分布动态变化规律。结果表明:增加取暖房、纪念品店、景观点等设施将提升观众在远郊大型冬季活动中的观赛体验。大型冬季活动的观众可接受的安检/检票、等公交的排队时间及步行到场馆的时间心理阈值为20 min,且观众更愿意选择步行时间短和服务设施数量多的路径;采用路径分流并提供路径实时信息,会使得分流路径上、下游节点的客流分布更为均衡平缓,有效减少客流聚集和长时间排队等待现象。

基于传递熵的空中交通航路网络拥堵传播特性分析

为深入剖析空中交通拥堵态势演变特性,辅助空中交通管制,确保航路网络的高效运行,本文研究空中交通航路网络拥堵传播相关问题。首先,选取交通流量、交通密度和交通汇聚度作为航段拥堵评估指标,建立FCM(Fuzzy C-Means)航段交通拥堵状态评估模型;其次,提出基于传递熵理论的航段拥堵传播模型,并分析拥堵与拥堵传播之间的关联性;然后,建立基于传播指数与显著性面积指标的关键航段识别方法;最后,采用广州区域管制扇区内实测数据检验本文提出方法的有效性。研究结果表明:综合考虑宏观和微观特征的拥堵识别模型能够有效地划分航段的交通状态;拥堵传播受时段和拥堵程度的影响较大,夜间时段,畅通态在拥堵传播中占据主导地位,信息传播量比日间高30%,且信息有效期更长;在白天,高拥堵航段在拥堵传播中占主导地位,其信息传播量约为低拥堵航段的1.2倍,但信息有效期较短;广州高空航路网络的拥堵传播呈现明显的时空差异性,拥堵传播高峰时段主要位于12:00-14:00和18:00-20:00,略早于拥堵高峰期,沪蓉航路等东西向主干航路是拥堵传播的关键航路。这些规律可为空中交通管制单位实施管制措施和优化航路结构等提供理论支持。

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。

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。

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.