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Deconstructing delay: A non-parametric approach to analyzing delay changes in single server queuing systems

机译:解构延迟:一种非参数方法,用于分析单服务器排队系统中的延迟变化

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This paper introduces an empirically driven, non-parametric method to isolate and estimate the effects that changes in demand and changes in throughput have on delay - in particular, arrival and departure flight delay at airport runways. Classic queuing concepts were used to develop a method by which an intermediate, or counterfactual, queuing scenario could be constructed, to isolate the delay effects due to shifts in demand and throughput. This method includes the development of a stochastic throughput function that is based entirely on data and has three key features. Firstly, the function relies on non-parametric, empirically-based probability distributions of throughput counts. Secondly, facility capacity needs not be explicitly defined, as it is implicitly included in the probability distributions of throughput. Thirdly, the throughput performance function preserves the effect of factors that cause capacity (and, therefore, throughput) to fluctuate over a given period. Temporal sequences of high, moderate, and low capacity are maintained between the observed and counterfactual scenarios. The method was applied to a case study of the three major New York area airports of LaGuardia (LGA), Newark Liberty (EWR), and John F. Kennedy (JFK), using operational data extracted from the Federal Aviation Administration's (FAA's) Aviation System Performance Metrics (ASPM) database. The focus was on the peak summer travel seasons of 2006 and 2007, as these airports experienced record levels of delay in 2007. The results indicate that decreases in both demand and throughput were experienced at LGA and EWR, although the decreases in throughput had more significant effects on operational delays as they increased overall at these airports. At JFK, the increase in departure throughput was not sufficient to offset the increase in departure demands. For arrivals, demand increased and throughput decreased. These trends caused a significant growth in delay at JFK between 2006 and 2007.
机译:本文介绍了一种经验驱动的非参数方法,用于隔离和估计需求变化和吞吐量变化对延误的影响,尤其是机场跑道的到达和离开航班延误。经典的排队概念用于开发一种方法,通过该方法可以构造中间或反事实的排队方案,以隔离由于需求和吞吐量变化而引起的延迟影响。此方法包括开发完全基于数据并具有三个关键功能的随机吞吐量函数。首先,该函数依赖于吞吐量计数的基于经验的非参数概率分布。其次,设施容量不需要明确定义,因为它隐含在吞吐量的概率分布中。第三,吞吐量性能函数保留了导致容量(因此吞吐量)在给定时间内波动的因素的影响。在观察到的和反事实的场景之间保持了高,中,低容量的时间顺序。使用从联邦航空局(FAA)的航空中提取的运营数据,该方法已应用于拉瓜迪亚(LGA),纽瓦克自由(EWR)和约翰·肯尼迪(JFK)这三个纽约地区主要机场的案例研究系统性能指标(ASPM)数据库。重点是2006年和2007年夏季旅行高峰期,因为这些机场在2007年出现了创纪录的延误水平。结果表明,尽管吞吐量的下降更为显着,但LGA和EWR的需求和吞吐量都出现了下降。这些机场在总体上增加了对运营延误的影响。在肯尼迪国际机场,离港吞吐量的增加不足以抵消离港需求的增加。对于抵港旅客,需求增加而吞吐量下降。这些趋势导致肯尼迪国际机场(JFK)在2006年至2007年之间的航班延误显着增加。

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