城市交通乘车路线的计算机查询

本文以南京市的公交路线为对象,在IBM4341计算机及IBM-PC/XT机上,实现了对任意给定的两个车站之间的最短乘车路径的查询(即换车次数和途经的车站数最少),查询的结果可以在图形屏幕上动态显示,同时还可在字符终端和打印机上输出路径上的各个站名、路径名以及换车地点。程序是用PASCAL语言编写的,便于移植。     

Dynamic airspace configuration method based on a weighted graph model

This paper proposes a new method for dynamic airspace configuration based on a weighted graph model. The method begins with the construction of an undirected graph for the given airspace, where the vertices represent those key points such as airports, waypoints, and the edges represent those air routes. Those vertices are used as the sites of Voronoi diagram, which divides the airspace into units called as cells. Then, aircraft counts of both each cell and of each air-route are computed. Thus, by assigning both the vertices and the edges with those aircraft counts, a weighted graph model comes into being. Accordingly the airspace configuration problem is described as a weighted graph partitioning problem. Then, the problem is solved by a graph partitioning algorithm, which is a mixture of general weighted graph cuts algorithm, an optimal dynamic load balancing algorithm and a heuristic algorithm. After the cuts algorithm partitions the model into sub-graphs, the load balancing algorithm together with the heuristic algorithm transfers aircraft counts to balance workload among sub-graphs. Lastly, airspace configuration is completed by determining the sector boundaries. The simulation result shows that the designed sectors satisfy not only workload balancing condition, but also the constraints such as convexity, connectivity, as well as minimum distance constraint.     

对流天气空域阻塞概率与阻塞指数模型

管制员高估对流天气的影响,发布不必要的流控指令,会使得航班过度延误;管制员低估对流天气的影响,会造成工作负荷的激增,也会使得航班飞行安全风险增大。为了准确评估空域受天气影响程度,减少管制员高估和低估天气影响次数,建立了特定方向对流天气空域阻塞概率模型和航路交通阻塞指数模型。测试结果表明,特定方向对流天气空域阻塞概率模型,用于衡量对流天气下交通流在各个方向被阻塞概率,为管制员决策是否允许航空器沿特定方向在扇区绕飞提供参考有实际意义;对流天气航路交通阻塞指数模型,用于衡量对流天气下沿航路交通流被阻塞的概率,为管制员决策是否允许航空器沿航路穿越对流天气提供参考是可行的。访谈数据表明,90%的参访者同意在这两个参数可得时,他们获取对流天气影响交通态势感知所需要的时间确实有所减少。     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.     

空中交通复杂性研究进展

空中交通复杂性是空中交通态势的本质属性，是空中交通管制工作负荷的主要驱动因素。空中交通复杂性的科学评价是实现空中交通精细化管理的关键，也是当前空管领域研究热点。本文介绍了国内外空中交通复杂性的研究团队，梳理了空中交通复杂性与工作负荷、安全水平等相互之间的区别和联系，并详细分析了现有研究的具体研究路线。在分析新一代空中交通管理系统特征的基础上，对空中交通复杂性的未来研究趋势和方向进行了展望。