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摘要翻译：
美国国家空域系统(NAS)是一个庞大而复杂的系统，由行政、控制中心、机场、航空公司、飞机、乘客等数千个相互关联的组成部分组成，其复杂性给管理和控制带来了许多困难。最紧迫的问题之一是航班延误。延误给航空公司带来了高昂的成本，引起了乘客的抱怨，给机场运营带来了困难。随着对系统需求的增加，时延问题变得越来越突出。因此，联邦航空管理局了解延误的原因并寻找减少延误的方法是至关重要的。造成延误的主要因素是始发机场的拥挤、天气、日益增长的需求和空中交通管理(ATM)的决策，如地面延误计划(GDP)。延迟是一种内在的随机现象。即使所有已知的因果因素都可以解释，宏观层面的国家空域系统(NAS)延误也不能从微观层面的飞机信息中确定地预测。本文提出了一个随机模型，利用贝叶斯网络对飞机延误各组成部分之间的关系以及影响延误的原因进行建模。从芝加哥奥黑尔国际机场(ORD)到哈茨菲尔德-杰克逊亚特兰大国际机场(ATL)出发航班延误的案例研究揭示了本地和系统层面的环境和人为因素如何共同影响延误的组成部分，以及这些组成部分如何导致最终到达目的地机场的延误。
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英文标题：
《Propagation of Delays in the National Airspace System》
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作者：
Kathryn Blackmond Laskey, Ning Xu, Chun-Hung Chen
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
The National Airspace System (NAS) is a large and complex system with thousands of interrelated components: administration, control centers, airports, airlines, aircraft, passengers, etc. The complexity of the NAS creates many difficulties in management and control. One of the most pressing problems is flight delay. Delay creates high cost to airlines, complaints from passengers, and difficulties for airport operations. As demand on the system increases, the delay problem becomes more and more prominent. For this reason, it is essential for the Federal Aviation Administration to understand the causes of delay and to find ways to reduce delay. Major contributing factors to delay are congestion at the origin airport, weather, increasing demand, and air traffic management (ATM) decisions such as the Ground Delay Programs (GDP). Delay is an inherently stochastic phenomenon. Even if all known causal factors could be accounted for, macro-level national airspace system (NAS) delays could not be predicted with certainty from micro-level aircraft information. This paper presents a stochastic model that uses Bayesian Networks (BNs) to model the relationships among different components of aircraft delay and the causal factors that affect delays. A case study on delays of departure flights from Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta International Airport (ATL) reveals how local and system level environmental and human-caused factors combine to affect components of delay, and how these components contribute to the final arrival delay at the destination airport.
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PDF链接：
https://arxiv.org/pdf/1206.6859
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