Econometric Analysis of U.S. Airline Flight Delays with Time-of-Day Effects

Hsiao, Chieh-Yu; Hansen, Mark

doi:10.1177/0361198106195100113

Cited by 26 publications

(17 citation statements)

References 3 publications

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“…Hotels therefore should be rewarded not only for producing good outputs (for example, revenue) but also for reducing their negative outputs. Research in other industries, for instance, has included 'pollution' when measuring the performance of industries, 'complications in operational procedure' when measuring the performance of hospitals and 'flight delays' when measuring the performance of airports (Tucker, 2004;Zaim, 2004;Hsiao and Hansen, 2006). All these are negative outputs that can affect the production process.…”

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confidence: 99%

Why Negative Outputs are Often Ignored: A Comprehensive Measure of Hotel Performance

Assaf

Cvelbar

2015

Tourism Economics

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Studies often overlook negative outputs when measuring hotel performance. This paper investigates the consequences of such omissions by comparing two performance models, one that includes negative outputs (customer complaints) and one that excludes negative outputs. The authors show that excluding negative outputs underestimates the efficiency and productivity results. The results also indicate important differences in the performance ranking of hotels when negative outputs were excluded from the model. The authors also found that hotel size and hotel classification have a minor impact on performance when accounting for negative outputs.

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confidence: 99%

Why Negative Outputs are Often Ignored: A Comprehensive Measure of Hotel Performance

Assaf

Cvelbar

2015

Tourism Economics

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“…Although the pattern varies with the airport, in general, the marginal cost of delay increases until a certain time in the afternoon, after which time the airport begins absorbing more of the delay. This effect is also shown by Tu (14) and Hsaio and Hansen (15), as they examined flight delays through econometric models, estimated the marginal cost of delay in relation to later delays, and demonstrated that earlier delays are more costly, without considering explicitly the propagation effects.…”

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confidence: 62%

Flight Delay Propagation Impact on Strategic Air Traffic Flow Management

Churchill

Lovell

Ball

2010

Transportation Research Record: Journal of the Transportation R

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Another dimension of delay propagation is the temporal transmission of delays over the course of a day at a single airport or at connected airports. When large delays are incurred in the morning, it becomes more likely that delays will take place later in the day. This phenomenon may be observed at both an airport level and a national level, with the severity of the propagation potentially related to the imbalance between demand and capacity. This is an illustration of a more general phenomenon in the study of queues: for oversaturated systems, the marginal price of a unit of delay earlier is much higher than it would be if that same delay were to occur later. This is easily illustrated in deterministic systems, but is also generally true for (more realistic) stochastic systems.A common approach for postoperational analysis of delay propagation is to apply a microscopic analytic model and then to aggregate the results. One of the original works in this area was conducted by airline personnel who used proprietary data to trace flights through their operations (1). Lovell et al. (2) and Churchill et al. (3) detailed a methodology that builds on the work of Beatty et al. (1) for differentiating flight data to separate propagated and queuing effects. Specifically, Lovell et al. (2) suggested subtracting upstream delays from empirical data records and translating downstream arrival schedules accordingly, and Churchill et al.(3) added to this idea the notion of accounting for delays as they occur rather than lumping them at the flight arrival time. Several other researchers have included similar label-conserving analytic models for tracking delays through a network (4-7 ). A non-label-conserving microscopic model was proposed by Mukherjee and Hansen (7), and it was shown to produce comparable results with less onerous data requirements.An alternative to the analytic models that still employs microscopic flight tracking is the use of simulation. Other researchers examined the effects of flight delay propagation through airline networks with simulation techniques to vary the conditions and to observe the results of various new and propagated delays (8).Several researchers have taken a more aggregate approach to tracking flight delay propagation. An aggregate statistical model for estimating temporal delay propagation trends at individual airports was proposed by Churchill et al. (9). That model is extended in this paper. Other research examined delay propagation through a network of several airports by using a Bayesian network approach to separate delay into components (10-12). A related statistical model was presented by Xu et al. to identify the quantity of delays that are generated or absorbed by each airport (13). In that research, two statistical models were presented to ferret out the so-called downstream ripple effect-one based on an autoregressive model and one on varying coefficient linear regression. In most of these models, the phenomenon under study is the same as that in the current paper.The aim of several ...

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“…The authors of Hsiao and Hansen (2006) formulate a model of the average daily flight delay, considering factors including queuing, especially time-of-day effects of arrivals, and weather at airports and on flights' routes. Through queuing theory, the authors find that a certain amount of queuing delay in the morning causes a much bigger impact on average daily delay than the same amount of delay in the evening does.…”

Section: Creating Robust Schedulesmentioning

confidence: 99%

Preemptive rerouting of airline passengers under uncertain delays

McCarty

Cohn

2018

Computers & Operations Research

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An airline's operational disruptions can lead to flight delays that in turn impact passengers, not only through the delay itself but also through possible missed connections. Much research has been done on crew recovery (rescheduling crews after a flight delay or cancellation), but little research has been done on passenger reaccommodation. Our goal is to design ways that passenger reaccommodation can be improved so that passengers can spend less time delayed and miss fewer connections. Since the length of a delay is often not known in advance, we consider preemptive rerouting of airline passengers before the length of the delay is known. Our goal is to reaccommodate passengers proactively as soon as it is known that a flight will be delayed instead of waiting until passengers have missed connections and to use known probabilities for the length of delay. In addition, we consider all of the affected passengers together so that we can effectively handle passengers' competition for available seats. We can give certain seats to people with short connections or those connecting to international flights. When there is one delayed flight, we model the problem as a two-stage stochastic programming problem, with first-stage decisions that assign passengers initial xi itineraries and second-stage decisions that reassign any passengers who are subsequently disrupted by the delay. We present a Benders decomposition approach to solving this problem. Computational results for this model are given, showing its effectiveness for reducing the length of passenger delays. When there is more than one delayed flight, we define a portfolio model which assigns passengers to portfolios that define their itineraries under all possible disruption outcomes. We focus on computational methods for solving this model. xii

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Econometric Analysis of U.S. Airline Flight Delays with Time-of-Day Effects

Cited by 26 publications

References 3 publications

Why Negative Outputs are Often Ignored: A Comprehensive Measure of Hotel Performance

Why Negative Outputs are Often Ignored: A Comprehensive Measure of Hotel Performance

Flight Delay Propagation Impact on Strategic Air Traffic Flow Management

Preemptive rerouting of airline passengers under uncertain delays

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