基于免疫进化算法的枢纽机场停机位调度模型

针对繁重机场日益严重的航班延误问题,以提高旅客的满意度和减少航班滑行时间为目标,提出了一种高效的停机位分配模型。模型基于免疫进化算法,以最少航班被分配到远机位和航班滑行时间的均方差最小为目标函数。模型可以用于在机场航班时刻已知的情况下,对不同时段的航班的停机位进行高效快速分配,通过国内某大型机场的实际运行数据进行计算及对比分析表明,模型和方法是有效可行的。     

资源受限条件下的离港航班停机位分配优化模型

停机位作为机场的重要资源,优化停机位分配策略,实现更优的航班运行效率和旅客满意度已成为研究的重中之重。针对研究牵引车、管制员等资源受限条件下的停机位分配问题,首先以航空器延误时间最少、靠桥数最大为目标,并将靠桥数最大转化为不靠桥数最小,建立停机位分配优化模型,采用启发式算法进行求解;其次搭建场面仿真模型以验证算法的可靠性;最后通过北京大兴国际机场进行实例验证。结果表明：相较于机场实际运行情况,本文所建立的停机位分配优化模型使航空器延误降低25.5%、靠桥率提升8.8%,本文所提出的停机位分配策略可以在资源受限条件下实现场面延误和靠桥率的优化。     

基于双层规划的机场场面运行规划研究

当今国内民航业迅速发展,大型机场航空器起降架次稳定增多,场面运行情况也随之更加复杂,这对机场场面运行规划提出了更高的要求。综合考虑停机位指派和滑行道调度两个优化问题,引入双层规划模型,上下层模型分别以停机位与机型的匹配度最高、滑行时间最短为优化目标,采用遗传算法进行求解,并以上海浦东机场为实例进行场面运行规划。结果显示,优化后的方案在近机位利用率与滑行时间上都得到了改进,验证了模型的有效性。     

基于强化学习的航空器机场智能静态路径规划

随着人工智能迅速发展以及“智慧机场”的提出,研究人工智能在机场如何有效地辅助机场管制人员,驾驶员指挥航空器在地面滑行具有重要意义。本文提出一种基于强化学习的滑行路径规划方法,构建航空器机场地面强化学习移动模型,并以海口美兰机场为案例采用 Python 内置工具包 Tkinter 进行场面仿真;在此基础上,考虑机场航空器滑行规则,采用 Off-Policy 中 Q-Learning 算法求解贝尔曼方程,实现航空器在 Model-based 环境中进行静态路径规划。结果表明：本文所提方法能够实现停机位到跑道出口智能静态路径规划     

基于虚拟排序与航空公司自主决策的航班推出策略研究

当机场场面出现拥堵时，新增的航班推出会增加机场的拥堵，同时增加燃油的消耗以及污染物的排放。提出了航班虚拟推出队列方法，以航班停机位等待代替航班滑行道排队等待，可以减少场面拥堵和污染物排放。在该方法的基础上提出了乘客等待时间最小及航班场面滑行成本最优的策略。仿真结果表明，对虚拟航班时隙的调整，可以降低旅客的等待时间、减少航空公司场面运行成本。     

基于特征选择的BP神经网络算法滑行时间预测

为准确预测离港航班滑行时间,基于数理分析同时段场面航空器滑行数量、平均滑行时间等因素对离港航空器滑行时间的影响。将皮尔逊相关系数与随机森林算法相结合减少冗余特征变量,建立基于BP神经网络的滑行时间预测模型,提高离港航空器场面滑行时间预测精度,并通过交叉验证证明预测结果的稳定性。预测结果表明：通过皮尔逊相关系数与随机森林组合模型进行特征选择可提高BP神经网络预测结果的精度,离港航空器的滑行时间预测误差在±5min内的占比由88.23%提升至92.26%,且预测效果较为稳定。模型可以精确预测离港航班的滑行时间,为机场运行提供决策依据。     

机场停机位分配问题的遗传算法

<正>机场停机位分配是指在考虑机型大小、停机位大小,航班时刻等因素的情况下,在一定时限范围内,由机场生产指挥中心为到港或离港航班指定适宜的登机口,保证航班正点不延误,为旅客上下飞机提供登机门(国外也称机门指派)。机场停机位分配是机场地面作业中的一项核心任务,为航班分配停机位包括航班占用停机位时间和占用具体停机位两项内容。1旅客等待时间最小化在繁忙机场的运行中,一般首先考虑将航班分配至     

设置绕行滑行道时的机场运行效率分析

目前,国外多个大型枢纽机场的绕行滑行道运行实例均表明,该滑行道能有效地提高机场运行效率。中国具有近距平行跑道的机场都有条件建设、运行绕滑。设置绕行滑行道能减少近距平行跑道机场的航空器跑道穿越次数,提高运行安全水平和航空器地面滑行效率,降低管制员工作负荷。通过建立有绕滑条件下进场航空器地面滑行路径的选择模型,并使用算例和对虹桥机场SIMMOD模拟计算证明了模型的实用性。计算分析表明,绕滑对减少航空器地面滑行时间作用显著,且机场高峰小时架次愈高作用愈明显。     

地面等待模型中的旅客延误时间成本分析与仿真

针对多机场地面等待问题MAGHP(Multi Airport Ground Holding Problem),深入研究了影响旅客延误时间的因素,并将其应用于数学模型中目标函数的计算。将航班延误时间转化为旅客延误时间,提出了一个在航班取消情况下减少旅客延误的新方法,并与多机场地面等待模型进行了比较,仿真结果表明,可以大幅度减少旅客延误时间。     

基于XGBoost的航空器动态滑行时间预测方法研究

对航空器进港和离港滑行时间进行精确的动态预测,可以有效提升机场的运行效率。首次提出基于XGBoost 的航空器动态滑行时间预测方法,该方法首先通过分析影响机场滑行时间的各类因素,构建可变滑行时间预测的关键特征指标;然后选取XGBoost 算法建立可变滑行时间预测模型,对模型的关键输入参数进行测试调整;最后将XGBoos...     

基于航空公司运行成本和公平性的停机位指派

缩短航空器在飞行区的滑行距离,可以降低航空公司运行成本,减少大气污染物排放;而均衡各航空公司的滑行距离,可以提高航空公司在机场运行的公平性,提升航空公司整体竞争力。从航空公司运行成本和公平角度出发,建立基于航空公司运行成本最小化和运行最公平的停机位多目标优化指派模型。采用生物地理学算法对模型进行仿真验证,对随机指派、成本最低、最公平、公平指数小于0.1,公平指数小于0.2等五种情况的仿真结果进行比较。仿真结果表明：当公平指数小于0.1时,停机位指派不仅有效降低航空公司运行成本,并大幅度提升航空公司间公平性,说明该模型能够更好的对停机位指派进行优化。     

基于空闲时间窗和多Agent的A-SMGCS航空器滑行路由规划

先进场面活动引导与控制系统(A-SMGCS)中的航空器滑行路由规划是一个典型NP难题。为解决航空器滑行路由规划的优化性和计算量之间的矛盾,提出一种基于空闲时间窗的路由规划方法,并利用多Agent系统(MAS)进行算法求解。首先,建立滑行资源图以对场面滑行区进行建模。其次,按照航班计划为航空器设置滑行优先级,并按优先级顺序依次规划路由,后规划的路由不破坏已有路由,即利用滑行路段的空闲时间窗进行规划。每次只需为一架航空器规划滑行路由,降低了问题的求解难度;通过搜索空闲时间窗获得路由使场面交通均衡分布,保证了路由规划的整体优化性。分析了空闲时间窗特性,指出空闲时间窗的可达性条件和避免同步资源交换冲突的条件。最后,设计MAS,把建立、维护和搜索空闲时间窗图的复杂集中式求解过程简化为通过路由管理Agent,航空器Agent和资源节点Agent相互协作实现对场面路由规划问题的分布式求解。仿真结果表明,设计的MAS能够快速找到空闲时间窗中的最优解;与固定预选滑行路径算法相比,航空器的平均滑行时间显著减少,最多可以节省19.6%的滑行时间。     

Methods for determining unimpeded aircraft taxiing time and evaluating airport taxiing performance

The objective of this study is to improve the methods of determining unimpeded (nom-inal) taxiing time, which is the reference time used for estimating taxiing delay, a widely accepted performance indicator of airport surface movement. After reviewing existing methods used widely by different air navigation service providers (ANSP), new methods relying on computer software and statistical tools, and econometrics regression models are proposed. Regression models are highly recommended because they require less detailed data and can serve the needs of general per-formance analysis of airport surface operations. The proposed econometrics model outperforms existing ones by introducing more explanatory variables, especially taking aircraft passing and over-passing into the considering of queue length calculation and including runway configuration, ground delay program, and weather factors. The length of the aircraft queue in the taxiway system and the interaction between queues are major contributors to long taxi-out times. The proposed method provides a consistent and more accurate method of calculating taxiing delay and it can be used for ATM-related performance analysis and international comparison.     

A new dynamic pushback control method for reducing fuel-burn costs: Using predicted taxi-out time

Long departure-taxi-out time leads to significant airport surface congestion, fuel-burn costs, and excessive emissions of greenhouse gases. To reduce these undesirable effects, a Predicted taxi-out time-based Dynamic Pushback Control(PDPC) method is proposed. The implementation of this method requires two steps: first, the taxi-out times for aircraft are predicted by the leastsquares support-vector regression approach of which the parameters are optimized by an introduced improved Firefly algorithm. Then, a dynamic pushback control model equipped with a linear gate-hold penalty function is built, along with a proposed iterative taxiway queue-threshold optimization algorithm for solving the model. A case study with data obtained from Beijing International airport(PEK) is presented. The taxi-out time prediction model achieves predictive accuracy within 3 min and 5 min by 84.71% and 95.66%, respectively. The results of the proposed pushback method show that total operation cost and fuel-burn cost achieve a 14.0% and 21.1%reduction, respectively, as compared to the traditional K-control policy.(3) From the perspective of implementation, using PDPC policy can significantly reduce the queue length in taxiway and taxi-out time. The total operation cost and fuel-burn cost can be curtailed by 37.2% and 52.1%,respectively, as compared to the non-enforcement of any pushback control mechanism. These results show that the proposed pushback control model can reduce fuel-burn costs and airport surface congestion effectively.     

民用飞机机载跑道入侵预警系统仿真验证

民用飞机在低能见度的机场跑道滑行时,存在发生跑道入侵的风险。国内外民航史记载了数次因跑道入侵事件导致的飞机在机场地面相撞而机毁人亡的严重事故。近年来,越来越多的人选择飞机作为长途出行的交通工具,使得全球大型国际机场的年吞吐量剧增,导致跑道入侵事件时有发生。各国民用航空管理组织对跑道入侵事件的关注度持续上升,航空公司需要在机上安装一套地面避撞系统,来避免跑道入侵事件的发生。本验证项目基于某大型机场的动态地图建立了一个机载跑道入侵预警系统原型,建立了飞机在机场滑行的平面四向运动模型,设定了不同安全等级的跑道入侵预警算法,定义了多种飞机跑道入侵场景,最终在MATLAB/Simulink和FlightGear中进行二维平面仿真和三维立体仿真,验证了系统解决方案的有效性和算法的正确性。     

图论最大流理论在机场登机口分配中的应用

为提高登机口的利用率、提升航空旅客出行的便捷性与舒适性、提高机场的运行效率及航空公司的运营效益,在图论中网络最大流理论的基础上,将旅客步行距离、机场资源运行效率、飞机最短过站时间、＂航班对＂、机型等作为约束条件,考虑航站楼布局、始发/终到及中转旅客数量等因素对登机口分配结果的影响,建立旅客登机口分配的优化网络模型,并对该算法的复杂度和最优性进行分析和证明。最后,运用实例来验证该方法在缩短旅客步行距离和提高机场运行资源利用率方面的可行性,该算法也可用于飞机停靠机位的优化安排。     

基于WPF的遥测CAS信息实时监控系统设计开发

在当前试飞模式下,立足于某型客机机组告警系统(Crew Alerting System,简称CAS)信息复杂、重构平台单一等不足,设计了一套CAS信息实时监控系统。系统基于WPF(Windows Presentation Foundation,简称WPF)框架,采用数据访问层、业务逻辑层和显示层组合的典型三层架构,以遥测脉冲编码调试(Pulse Code Modulation,以下简称PCM)数据为输入,结合DataBinding数据驱动模型和XAML等技术实现了CAS信息的实时解析、传输、分发及显示。同时,极大地降低了系统间的耦合性,提高了实时性能和系统的可维护性。通过滑行、首飞等试验的充分验证,系统实现了该型客机CAS信息的100%地面复现,为后续民机试飞测试、监控技术的创新提供了参考依据。     

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.