Characterizing and classifying historical days based on weather and air traffic
Characterizing and classifying historical days based on weather and air traffic
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根据天气和空中交通对历史日期进行描述和分类
DOI：
10.1109/dasc.2015.7311341
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Chris Skeels
中科院分区：
文献类型：
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作者：
K. Kuhn;Akhil Shah;Chris Skeels
本文描述了我们对相似天数组的识别，其中相似性是根据与空中交通流量管理举措规划相关的条件来定义的。这里的工作代表了为航空公司运营中心的调度员和联邦航空管理局空中交通管制系统指挥中心的官员构建决策支持工具的第一步。我们工作的副产品是对航空系统研究中日历日进行分类的合理方法的分类。航站区/机场预报和航空例行天气报告数据分别描述机场的预报和观测天气。航空系统性能指标数据总结了机场运营。在可用数据中定义特征的合理方法包括：应用专家判断，使用主成分分析来捕获日间差异，以及通过加权平均值总结特定天气变量的观测结果，其中权重反映空中交通水平。我们的首选功能基于专家判断，包括预定到达和起飞的计数、最低预报能见度、最大预报跑道侧风，以及纽约地区三个最繁忙机场（或在侧风情况下，四个主要跑道）在关键时间段的降雪、雷暴和降雨预报。航空系统中的许多工作都使用有关不同时间点交通流管理举措是否存在的记录作为标签数据。相反，我们使用围绕 Medoids 的分区算法进行聚类，因为我们不希望对当前决策进行建模或使用欧几里德距离度量，但希望将所有日期分配给一个聚类。我们的结果表明我们的特征数据结构较弱；日期不会被排列成少数几天的簇，每个簇都包含彼此非常相似的日期。
This article describes our identification of sets of similar days where similarity is defined in terms of the conditions relevant to the planning of an air traffic flow management initiative. The work here represents a first step toward the construction of a decision support tool for dispatchers at airline operations centers and officials at the Federal Aviation Administration Air Traffic Control System Command Center. A side product of our work is an taxonomy of reasonable approaches for categorizing calendar days in aviation systems research. Terminal Area / Aerodrome Forecast and Aviation Routine Weather Report data describe forecast and observed weather at airports, respectively. Aviation System Performance Metrics data summarize airport operations. Reasonable methods for defining features within available data include: applying expert judgment, using Principal Component Analysis to capture variance among days, and summarizing observations of specific weather variables via weighted averages where weights reflect levels of air traffic. Our preferred features, based on expert judgment, include counts of scheduled arrivals and departures, the minimum forecast visibility, the maximum forecast runway crosswinds, and forecasts of snow, thunderstorm, and rain at the three busiest airports (or, in the case of crosswinds, at four key runways) in the New York area during key blocks of time. Many efforts in aviation systems use records on the presence or absence of traffic flow management initiatives at various time points as label data. We instead use a Partitioning Around Medoids algorithm for clustering as we do not wish to model current decision making or use a Euclidean distance metric but do wish to assign all days to a cluster. Our results indicate weak structure in our feature data; days are not arranged into a handful of clusters of days that each contain days which are strongly similar to one another.