译者说：这篇文章是由欧控主席发布，其中简略而直观的说明了当前空中交通管理系统在细节的表现力上的掣肘与人工智能的潜能。
Editorial by Eamonn Brennan Director General EUROCONTROL
Air traffic management (ATM) is under great pressure at present. The drivers of the industry are safety, capacity, cost of the service, efficiency (direct routings at an optimal level) and the environment. The relative importance of these fluctuates over time – a few years ago, cost was the main influence on policy; now it is capacity, but with all the other drivers close behind and eager not to be forgotten.
当前，空中交通管理系统面临巨大压力。其推进力量是安全，容量，服务成本，效率（定义为：以最好的方式直接飞往目标）以及环境。这些因素的重要性会随着时间波动——不久以前，成本是影响民航政策制定的关键因素，现在是容量，但其他所有因素都在紧追其后。
At the same time traffic is increasing (11 million flights in 2018), extreme weather events are becoming more common and we also see airspace closures – typically on the fringes of Europe, or even further away (such as in Pakistan) but all having an impact on traffic flows in the European network, which itself is becoming more inter-connected and more inter-dependent. A staffing problem at one control center, a thunderstorm, or a blocked runway at a busy airport can create disturbances to traffic patterns hundreds of kilometers away, like ripples from a stone thrown in a lake.
与此同时，交通流量正在增长（2018年增长了1100万航班），极端天气的频发，以及经常出现的空域关闭（不论是常见的是欧洲的边缘，或者是更远的地方（例如巴基斯坦））都会对欧洲网络整体的交通流量造成了影响，所以，欧洲网络正在变得越来越相互关联、越来越容易相互影响。一个管制中心的人为问题，一个雷暴或者一个繁忙机场的跑道堵塞，都会对千百公里之外的交通模式造成干扰，就像一个石头扔进了湖里而产生的波纹。
And if by chance we have not one stone in our lake, but two or three at the same time, then the impact of those ripples becomes exponentially more difficult to predict.
恰好地，如果同时扔入湖中的石头是两或三个，那么波纹之间的影响会变得极难预测。
For me, this is a good example of why we need to embrace artificial intelligence (AI) and to explore how it can help us in ATM. So this edition of Skyway has some very practical examples of what AI actually means for ATM. These include improving the accuracy and speed of existing tasks, such as processing 30,000 flight plans every day and minimizing the need for human intervention. We also need to improve the predictability of traffic – looking outside our borders and also using new data flows from airports to find out where the network may become overloaded.
对我来说，这是一个很好的例子来说明为什么我们需要接受人工智能，并且去探索它将如何在ATM上帮助我们。所以这一版本的Skyway（杂志）举了许多实用的例子说明人工智能实际上对ATM意味着什么，这些例子包括了改进当前任务的准确性和速度，例如每天处理30000架航班计划以及最小化人为活动的参与。我们也需要提高交通的可预测性——（看看我们的边界之外？）以及使用新的机场数据流去发现网络过载之处。
For the future, we are moving towards a more interactive approach with aircraft trajectories being updated in real-time to adapt to changes. It is closer than one might think and it will certainly mean a step forward in the capabilities of our systems to cope with the flood of data and to make intelligent decisions. This will be essential if we are to handle the levels of traffic predicted, as well as to cope with the new types of traffic, such as drones, that are on the horizon.
对于未来，我们正在追寻一种将航迹引入的交互性方法，此时航迹实时更新以适应变化。这种情况比人们想象的更近，并且这意味着我们系统将在容量上做出一些进步，以处理大量数据和智能决策，如果我们想要处理这个级别的交通预测，那么这将是必要的，在面对新型交通时也是如此，尤其是即将来临的无人机。
However, it isn’t all about the technology. EUROCONTROL is a great believer that the kind of change needed to ensure successful uptake of AI should happen from the inside. As part of that, we have recently launched the European Aviation Artificial Intelligence High Level Group, bringing together representatives from both public and private sectors including EU bodies, international organisations in aviation and aviation industry representatives. Working together, the group is committed to develop a roadmap and practical recommendations to accelerate the uptake of AI in our sector and make sure that we can harness its potential for the good of our industry.
然而，这不仅仅只在技术。欧控坚信，人工智能能够被成功应用在ATM上的这种改变的关键应该是来自内部的。所以我们最近启动了欧洲航空人工智能高级小组，成员来自欧控、航空国际组织和航空业的各方代表。小组将共同工作致力于发展一个前沿路径和实践推荐来加速AI在我们领域的应用，并确保我们能够控制它的潜力以益于航空业。
