Airline Delays Research Articles

PREDICTING FLIGHT DELAYS WITH ERROR CALCULATION USING MACHINE LEARNED CLASSIFIERS

Worldwide, flight delays are becoming a major issue for the airline business. Due to air traffic congestion brought on by the airline industry's expansion over the last 20 years, flights have been delayed. In addition to hurting the economy, flight delays also have a detrimental effect on the environment since they increase fuel consumption and gas emissions. Thus, it is essential to take all reasonable precautions to avoid flight delays and cancellations. This paper's primary goal is to forecast an airline's delay utilizing a variety of variables. Thus, it is necessary to perform forward-looking analysis, which encompasses a variety of algorithmic predictive analytics approaches that use historical and current data to create models that are used for forecasts or simply to look at future delays using machine learning algorithms like Python 3's Gradient Boosting Regression technique, Bayesian Ridge, Random Forest Regression, and Logistic Regression. This will make it easier for the user to forecast whether an aircraft will arrive on time or not. Additionally, delay prediction analysis will assist airline industries in reducing their losses.

Factors Influencing Airline Delays of United States

Factors Influencing Airline Delays of United States

Airline delay propagation: Estimation and modeling in daily operations

Airline companies try to increase their revenues, service level, and customer satisfaction in a highly competitive global sector. Airline schedule planning is crucial for airline companies to reach these objectives. Airline schedules are usually constructed assuming that there will be no disruption. But in reality, there are plenty of incidences such as weather conditions, mechanical failure, air traffic, and security issues that cause delays and disrupt daily operations. Even though it is impossible to avoid the delay completely, there are ways to decrease the propagation of the delay. To cope with delay propagation, airlines insert idle time, known as slack, between flights in the schedule. However, idle time means inefficient use of aircraft resources. Thus, adjusting the idle time in the schedule dynamically during daily operations is a critical task for planning departments. In this study, flight time rescheduling and aircraft swapping are used to decrease the expected delay propagation. By using these two options, the scheduled slack is clustered at flights that are prone to delay propagation. We aim to reduce the negative consequences of delay proactively while keeping the total slack constant in the schedule. Keeping the slack constant helps reduce other adverse network effects and enables the rest of the plan to be still intact for the future. We propose to use multivariate kernel density estimation to estimate the probability of independent delay from flight data and argue that this is a practical and effective way of estimating such distributions for daily airline operations. We use that estimation in two mathematical programming formulations: the single layer model, and the single layer model with aircraft swapping option to minimize the expected propagated delay. Since the latter model is a non-linear model, we also introduce an approximation for it to overcome the computational issues in solving large instances of the problem. After illustrating our approach on a small set of data, we report our computational results using flight schedule data from Turkish Airlines augmented with weather related information. We argue that the proposed models help decrease the expected delay propagation by up to 90% allowing a 15-min change in the schedule and swapping aircraft when necessary.

Comparative study of ensemble machine learning techniques for airline delay prediction

Comparative study of ensemble machine learning techniques for airline delay prediction

Aircraft routing clusters and their impact on airline delays

Airlines operate thousands of aircraft routings every day with a multitude of options to connect single flights integrated in a routing. Since airline delays entail significant costs for airlines and to societies, we aim to identify how far individual aircraft routings differ in on-time performance. We apply a novel approach to cluster aircraft routings and compute the routing strength to quantify the structure of aircraft routings. We then develop an econometric model to analyze the impact different routing clusters have on airline delays. As a key finding, routings with higher strengths tend to cause more delays. Our clustering of routings shows that hybrid routing structures and intense shuttling of flights between a few airports in a routing increases delays compared to routings structured star- or string-like. We also find major differences across airlines in the way routings are structured.

Reassigning Departure Slots with Preferences of the Airline and Passengers

The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.

Hedonic preference of airline passengers

The purpose of this study is to determine which airlines Davao City-bound and outbound passengers prefer. Passenger information was acquired via survey questionnaires employing the Likert scale. In this study, non-experimental quantitative research employing a casual research method was employed. Using descriptive and informal research methodologies, this study investigated whether demographic variables and airline service quality influence the airline service choices of Davao City passengers. This investigation employs artificial neural network research to determine which factors influence airline preference. The majority of air carrier respondents are between the ages of 21 and 35, are predominantly female, and are employed by private companies. The airline preferences of passengers are influenced by price, cabin services, flight schedule, safety, on-time performance, and employee conduct. Important airline demographics included age, occupation, and sex. Price is the most significant criterion when selecting an airline, followed by flight schedule, on-time performance, and employee conduct. Flights should offer discounts to students and senior citizens. They may adjust fares based on the findings. To attract a broader age range of customers, airlines should strategically organize their marketing and focus on specific market niches. To increase sales, airlines may consider offering discounted, low-cost, or one-peso fares during certain times. To assist time-sensitive travelers in reaching their destinations in the event of airline delays or cancellations, alternative flights are advised. To gain a deeper understanding of airline carrier selection, research should be extended, employing a larger sample or alternative statistical methods.

Social cost of airline delays: Assessment by the use of revenue management data

Social cost of airline delays: Assessment by the use of revenue management data

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays. The proposed model is estimated using 2019 marketing carrier on-time performance data compiled by the Bureau of Transportation Statistics (BTS) for 67 airports in the continental U.S. The delay data is augmented with a comprehensive set of independent variables including traffic conditions at the origin and destination airports in the hours preceding flight departure and arrival, trip-level attributes, weather variables for the entire flight duration, and spatial and temporal factors. The model estimation results highlight that the Joe copula model with parameterization provides the best data fit. The model performance is further established to be excellent using a holdout sample. Finally, to illustrate the applicability of the model for prediction and highlight the impact of independent variables, we perform a prediction exercise under a host of hypothetical scenarios. The illustration provides a mechanism for employing the proposed model as a tool for airline-carrier-level or airport-level delay prediction analysis using weather forecasts while controlling for a host of independent variables.

Airline delay propagation: A simple method for measuring its extent and determinants

This paper offers a simple approach for identifying propagated departure delays and measuring their contribution to arrival delays. Under our approach, a propagated departure delay occurs when the arrival delay of the inbound flight exceeds the subsequent flight’s ground buffer. The size (or frequency) of such propagated delays relative to the size (or frequency) of arrival delays then measures the contribution of propagated delays to late arrivals. This approach differs from earlier attempts to quantify the contribution of delay propagation since it focuses on an individual flight and its immediate predecessor, without attempting to trace the sources of delay propagation back through the entire sequence of prior flights. The paper’s empirical results show that the contribution of propagated departure delays to arrival delays depends on several key determinants.

Optimal Contract for Reducing Flight Delays in EU: In the Context of SESAR

In the context of the SESAR (Single European Sky Air traffic management Research) Joint Undertaking, the role that the air navigation service provider (ANSP) could play has been reconsidered. ANSP manages traffic and deals with potential conflict situations and external events, which have led to the reorganization of the air traffic. The modification of traffic inevitably leads to delays for airlines, which is costly. In this study, we suggest that ANSP could provide a costly delay reduction service to airlines. Indeed, if ANSP has several solutions for traffic conflict resolution or reorganization, there is room for a choice between these solutions using additional criteria. Our study thus proposes an original model in which we determine the optimal design of a delay reduction contract signed between welfare- or profit-maximizing ANSPs and a monopoly airline. We give some comparative statics, in particular, the evaluation of the impact of a modification in safety standards on the contract.

THE IMPACT OF ECONOMIC ANALYSIS METHODS ON PROJECT DECISION-MAKING IN AIRPORT PAVEMENT MANAGEMENT

Airports are a part of the world transportation network. Huge investments are made annually for airport pavement construction, maintenances and rehabilitations. The idea of integrating life-cycle cost analysis (LCCA) and life cycle assessment (LCA) is the latest approach to develop a method for assessing pavement sustainability. In this regard, research on economic evaluation analysis methods has resulted in the development and improvement of pavement management systems (PMS). This paper compares two main economic evaluations which mainly could use in LCCA namely net future value (NFV) and net present Value (NPV). To indicate the effect of economic evaluation a case study is examined. In this research LCCA comprises three main components which are direct costs, indirect costs, and salvage value. Airport Revenue Reduction Cost (ARRC) and Airline Delay Cost (ADC) considered as two specific indirect/user costs. The results show the impact of different economic analysis method on project decision-making where the use of crack sealing overlay (CSOL) is 35.8% and 28.3% more cost-effective than Portland cement concrete (PCC) and hot-mix asphalt (HMA), respectively.

Analytic Hierarchy Process-Based Airport Ground Handling Equipment Purchase Decision Model

The Airport ground handling services (AGHS) equipment supplier provider selection requires a safety guarantee in terms of the daily operations AGHS provider. AGHS providers seek to avoid aircraft damage and airline delays and ensure the provision of reliable and high-quality services. The primary objective of this paper was to develop purchasing decision model of the analytic hierarchy process (AHP), AHP-fuzzy linear programming (FLP), and AHP-Taguchi loss function (TLF) multi-choice goal programming (MCGP) purchase decision models to help the AGHS purchasing managers in selecting the best AGHS equipment supplier provider. The constructed models were assessed, and results obtained for the AHP-FLP and AHP-TLF-MCGP models were compared. We conducted a real-world example of supplier selection by an AGHS company by using the proposed models. The proposed model provides useful information and has practical value for AGHS providers.

Airline Delay Propagation: A Simple Method for Measuring its Extent and Determinants

This paper offers a simple approach for identifying propagated departure delays and measuring their contribution to arrival delays. Under our approach, a propagated departure delay occurs when the arrival delay of the inbound flight exceeds the subsequent flight’s ground buffer. The size (or frequency) of such propagated delays relative to the size (or frequency) of arrival delays then measures the contribution of propagated delays to late arrivals. This approach differs from earlier attempts to quantify the contribution of delay propagation since it focuses on an individual flight and its immediate predecessor, without attempting to trace the sources of delay propagation back through the entire sequence of prior flights. The paper’s empirical results show that the contribution of propagated departure delays to arrival delays depends on several key determinants.

Bigdata Analysis on Airline Delay and Cancellation

In recent years, there has been a tremendous increase in the number of air passengers. These increasing figures have enhanced problems, ultimately causing flight delays. Delay in flights is recognized not only the usual factor but also a bothersome experience offered to customers. These delays lead to loss of money to the travelers. Additionally, it also affects the business value and status of the aviation industry. The delays can be caused due to many factors like weather conditions, technical problem, air traffic etc. These concerned parameter makes it important in carrying out analysis based on past trends of delays which can be further divided into daily, monthly and yearly intervals. This project makes use of big data technologies like Apache Pig, hive and Hadoop Distributed File System (HDFS). These technologies would be helpful in determining delay problems faced by the aviation industry where an appropriate decision can be implemented and furthermore would be benefiting customers and industrialists of the aviation domain.

Measuring the Length of a Two-Lane Curbside Based on Traffic Features

The geometric design of airport curbsides is critical for both airport planners and passengers as it affects airport ground traffic capacity and creates airline delays. Rational design of curbsides can yield increased efficiency in the use of road space and time resources. Yet a limited number of studies have reported the impact of traffic features on curbside’s geometric design. This paper provides a two-lane curbside measurement with a simulation model to calculate the length of curbsides, which solves the curbside berth supply and curbside length calculation problems using a refined curbside parking model (RCPM) with constraints of a car-following (CF) model, as well as uniformly distributed speed, dwell time, and parking demand; it thereby unveils the detailed microscopic mechanisms of interaction of traffic features and curbsides’ length. The required lengths of curbsides under various parking demands are obtained after a set of numerical simulations. The result further reveals the significant impacts of car-following and braking behaviors, particularly under the huge parking demands on curbside traffic flow. It also demonstrates that a certain delay is created and a suboptimum level of service (LOS) occurs even though satisfying all parking demands, which indicates that lengthening the curbside and increasing parking supply do not necessarily improve the LOS.

Airport Dominance, Route Network Design and Flight Delays

In this paper, we use detailed information of weather conditions, Eurocontrol regulations and strikes to examine the main drivers of flight delays in six Spanish airports in the period 2017–2018. We show that external shocks are an important driver of delays for low-cost and dominant airlines at large airports. Dominant airlines have better on-time performance when they operate hub-and-spoke networks, although this can be partially explained by their more frequent use of schedule padding. Finally, we find no direct evidence that competition at route level affects delays, although padding is more frequent when route competition is weak.

Impact of Ground Handling on Air Traffic Volatility

Ground handling services constitute an important element of airline operations and significantly affect traffic stability and punctuality. In this article, the existing and potential impact of airline handling on air traffic volatility is reviewed from the point of view of airlines and ground operations. The issues of airline expectations towards ground handling agents (including handling rates, turnaround time, passenger services, and ramp services) are explored. In addition, the impact of an airline’s schedule and the volatility of its operations on the performance and operational requirements of handling agents is discussed, including actions required by handling agents in response to the above challenges. The mechanism of how the volatility of an airline’s schedule and its operations may impact the volatility of ground operations (directly and indirectly) is considered. The statistics of airline delays caused by ground operations are presented and discussed. The issue of the correctness of air traffic delays reporting by airlines is investigated.Furthermore, this article investigates internal factors of ground handling agents and their impact on air traffic volatility. The existing and potential considerations discussed include staff management issues (in particular, employee rotation resulting in staff shortages and service quality, including punctuality), resources management, the ground service support equipment (including new developments aiming at limiting ground safety incidents), and their impact on performance.

Antecedents of flight delays in the Australian domestic aviation market

Flight delays are widespread and growing around the world. Using public domain data of the airlines in Australia's domestic aviation market, this study investigates the antecedents of flight delays, and how airline operations contribute to such an important issue in the aviation industry. Applying the Hausman-Taylor regression method, the estimations suggest that the variables of the airline capacity decision are meaningful determinants of flight delays. Our findings inform that more frequent flights and larger aircraft are associated with more flight delays. A higher level of route congestion leads to more flight delays, especially for the low-cost carriers. Jet fuel price is identified as a driver, leading to more flight delays for the legacy airlines compared to the low-cost carriers. Keen competition between the airlines actually helps to improve the flight service quality, through fewer flight delays.

Minimizing airline and passenger delay cost in airport surface and terminal airspace operations

This research studies the effectiveness of incorporating airline and passenger delay cost (APDC) into an integrated airport surface and terminal airspace (ASTA) traffic management system. Most air traffic management systems typically schedule aircraft with an assumption that all flights want to be operated along the minimum fuel trajectory. However, airlines and passengers may have other preferences that can significantly influence flight schedules. Therefore, the objective of this research is to investigate the effect of incorporating APDC to ASTA scheduling, while ensuring safety. A mixed integer nonlinear programming model (MINLP-APDC) and a swap separation violating aircraft heuristic model (SSVA-APDC) are developed to minimize the cost of delays for airlines and passengers. The proposed approaches are compared to the first-come, first-serve heuristic and two integrated scheduling algorithms for ASTA operations: 1) minimizing runway makespan (MINLP-RM); and 2) minimizing flight delays (MINLP-FD). The experimental results show that the proposed approaches save at least 1.2% APDC compared to other approaches. The proposed approaches can also achieve at least 3.0% fewer flight delays than the MINLP-RM model without increasing either runway or schedule makespan. Compared to MINLP-FD, the MINLP-APDC model increases flight delays by on average 3.7% while the SSVA-APDC model achieves on average 15.1% more flight delays. Although the MINLP-APDC model outperforms the SSVA-APDC heuristic in terms of APDC and flight delays, it requires more than 30 min of computational time. Meanwhile, the SSVA-APDC heuristics requires only a few seconds to provide a feasible flight schedule, which makes it more practical.