Data Analysis of Delays in Airline Networks
Data Analysis of Delays in Airline Networks
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DOI：
10.1007/s12599-015-0391-3
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发表时间：
2015-06
影响因子：
7.9
通讯作者：
Lucian Ionescu;C. Gwiggner;N. Kliewer
中科院分区：
文献类型：
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作者：
Lucian Ionescu;C. Gwiggner;N. Kliewer
成本优化的航空公司资源调度往往意味着在不可预见的中断情况下缺乏延误容忍度，例如延迟登机，技术缺陷或机场和空域拥挤。因此，考虑时间性和鲁棒性已成为鲁棒资源调度的一个重要课题，近年来已开发出各种复杂的调度方法。然而，这些方法依赖于关于延迟发生的假设。更好地理解延迟机制可能会导致更好的成本效益和鲁棒性之间的权衡，因此，本文的目的。我们提供了一个数据驱动的检测决策规则的白天延迟的趋势，根据时空属性。重点是可解释的规则，其预测准确性与随机森林进行比较，作为一种非参数、自动化建模方法。所得到的结果给洞察到的性质，主要的延迟发生和延迟预测的背景下，强大的资源调度的方法潜力。
Cost-optimized airline resource schedules often imply a lack of delay tolerance in case of unforeseen disruptions, e.g. late check-ins, technical defects or airport and airspace congestion. Therefore, the consideration of timeliness and robustness has become an important topic in robust resource scheduling and a wide range of sophisticated scheduling approaches has been developed in recent years. However, these approaches depend on assumptions made concerning delay occurrences. A better understanding of delay mechanisms may lead to a better trade-off between cost-efficiency and robustness and is therefore the purpose of this paper. We provide a data-driven detection of decision rules for daytime delay trends, depending on spatio-temporal attributes. The focus is on interpretable rules whose prediction accuracy is compared to random forests as a non-parametric, automated modeling approach. The obtained results give an insight into both the nature of primary delay occurrence and the methodical potential of delay prediction in the context of robust resource scheduling.