Search

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine. [ABSTRACT FROM AUTHOR]

Nowadays the phenomenon of flight delays and cancellations is becoming more and more serious. Flight delays and cancellations not only waste transportation resources, but also affect passengers' travel plans, which cause an increase in passenger discontent and complaint rates[2]. The passengers' dissatisfaction and distrust of airlines seriously damage the airlines' corporate reputation and then affect passengers' loyalty. Therefore, based on the information of 5 million flight data in the United States in 2016, we predicted the flight cancellations based on four classification algorithms: logistics regression, support vector machine (SVM), naive bayes and decision tree. We compared the training time of these algorithms by changing the number of nodes in the Spark cluster[9], and we also compared the classification accuracy, AUC (Area Under Curve) and PR(Precision-Recall). Experimental results show that: (1) When the number of nodes was 1, the average training time of these algorithms was 460 seconds. When the number of nodes was 3,5,7,9, the corresponding average training time were 207.25 seconds, 89.75 seconds, 44 seconds, 42 seconds, and when the number of nodes was 11, the average training time was 43.5 seconds; (2) Starting from 7 nodes, as the number of nodes increased while the decline rate of the average training time slowed down; (3) When the number of nodes was 1 and 3, the training time of naive bayes was the shortest, which were 386 seconds and 185 seconds respectively. When the number of nodes was 5,7,9 and11, the training time of SVM was the shortest, which were 72 seconds, 21seconds, 19 seconds and 18 seconds respectively; (4) the classification accuracy of SVM and decision tree was almost 90%, but naive bayes was just about 50.8% and logistics regression was only about 62.4%; (5) Both the AUC and PR of the decision tree algorithm were the highest: AUC was 0.558 and PR was 0.439. Therefore, the decision tree algorithm was the most suitable for predicting whether the flight would be cancelled or not, and there was a 90% probability that the prediction would be successful. [ABSTRACT FROM AUTHOR]

Flight on-time performance is one of the most important issues in the National Airspace System, a very complex and dynamic system. To avoid negative impacts to the aviation industry, the Federal Aviation Administration has set a long-term objective of understanding and mitigating flight delays. Building an effective and accurate prediction model of flight-delay incidents will help airport executives make the best decisions in delay scenarios. This article utilized two advanced prediction methods to predict the probability of a flight-delay incident--data mining using the decision tree and data mining using Bayesian inference. Prediction models were built using flight on-time performance data collected from different sources. The results indicated important airport-related factors and their effects on the flight on-time performance. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to analyze the extent to which major US airlines respond to one another in quality of service improvements.Design/methodology/approach Utilizing monthly data, the authors estimate a five-equation vector autoregressive model to determine which airline leads or follows others in quality of service improvements.Findings This study found that the five major airlines make interrelated decisions when responding to customer complaints concerning flight problems, over-sales, reservations, ticketing, boarding, and customer service. Every airline either responds to or influences the changes in customer complaints faced by at least one other airline, while some airlines do both. However, only one such relationship was found when examining if airlines change the percent of flight delays they have control over in response to changes in flight delays faced by another airline.Practical implications The number of passenger complaints against an airline can be influenced by the airline, as can the number of carrier-caused flight delays. The industry leaders in responsiveness to consumer complaints are US Airways and United. However, airlines do not, as a group, respond to the carrier-caused delays of their competitors. The prescription to improve airline service vis-à-vis flight delays is simple: tell passengers why flights are delayed. To protect or gain market share, airlines would compete for customers by minimizing flight delays in a similar manor to how they respond to customer complaints.Originality/value No other paper that the authors are aware of has addressed the issue of identifying leaders and followers in the US airline industry regarding changes in service quality as reflected by changes in passenger complaints and flight delays. [ABSTRACT FROM AUTHOR]

Highlights: [•] We compute time- and carrier-specific hubbing concentration indices for US airlines. [•] We quantify the impact of changing hubbing concentration on three delay indicators. [•] Increasing hubbing concentration contributes to longer excess travel times. [•] We find no influence of changing hubbing concentration on delays against schedule. [•] Our results indicate that airlines use buffers to mitigate passenger-perceived delays. [ABSTRACT FROM AUTHOR]

Airline flight delays have come under increased scrutiny lately in the popular press, with the Federal Aviation Administration data revealing that airline on-time performance was at its worst level in 13 years in 2007. Flight delays have been attributed to several causes such as weather conditions, airport congestion, airspace congestion, use of smaller aircraft by airlines, etc. In this paper, we examine the impact of the scheduled block time allocated for a flight, a factor controlled by airlines, on on-time arrival performance. We analyze empirical flight data published by the Bureau of Transportation Statistics to estimate the scheduled on-time arrival probability of each commercial domestic flight flown in the United States in 2007 by a major carrier. The structural estimation approach from econometrics is then used to impute the overage to underage cost ratio of the newsvendor model for each flight. Our results show that airlines systematically "underemphasize" flight delays, i.e., the flight delay costs implied by the newsvendor model are less than the implied costs of early arrivals for a large fraction of flights. Our results indicate that revenue drivers (e.g., average fare) and competitive measures (e.g., market share) have a significant impact on the scheduled on-time arrival probability. We also show that the scheduled on-time arrival probability is not positively affected by the total number of passengers on the aircraft rotation who could be affected by a flight delay, or the number of incoming and outgoing connecting passengers on a flight. Operational characteristics such as the hub and spoke network structure also have a significant impact on the scheduled on-time arrival probability. Finally, full-service airlines put a higher weight on the cost of late arrivals than do low-cost carriers, and flying on the lowest fare flight on a route results in a drop in the scheduled on-time arrival probability. [ABSTRACT FROM AUTHOR]

Abstract: US airline passengers increasingly have access to flight delay information from online sources. As a result, air passenger travel decisions can be expected to be influenced by delay information. In addition, delays affect airline operations, resulting in increased block times on routes and, in general, higher carrier costs and airfares. This paper examines the impact of flight delays on both passenger demand and airfares. Delays are calculated against scheduled block times as well as against more idealized feasible flight times. Based on econometric estimations, welfare impacts of flight delays are calculated. We find that flight delays on a route reduce passenger demand and raise airfares, producing significant decreases in both consumer and producer welfare. Since producer welfare effects are estimated to be three times as large as consumer welfare effects, we conclude that from an economic efficiency rationale, airlines should be required to pay for the bulk of flight delay remediation efforts. [Copyright &y& Elsevier]

Abstract: This paper simulates airline strategic decision making and its impact on passenger demand, flight delays and aircraft emissions. Passenger flows, aircraft operations, flight delays and aircraft emissions are simulated for 22 airports in the US, under three airport capacity scenarios. The simulation results indicate that most system-wide implications for operations and environmental impact seem to be manageable, but local impacts at congested hub airports may be significant. The response of the air transportation system to avoid airports with high delays could significantly impact passenger demand and air traffic for these and directly dependent airports. The simulations also suggest that frequency competition effects could maintain flight frequencies at high levels, preventing a significant shift toward larger aircraft, which would otherwise reduce the impact of the capacity constraints. [Copyright &y& Elsevier]

In the United States, most airports do not place any limitations on airline schedules. At a few major airports, the current scheduling restrictions (mostly administrative measures) have not been sufficiently strict to avoid consistent delays and have raised debates about both the efficiency and the fairness of the allocations. With a forecast of 1.1 billion yearly air travelers within the US by 2015, airport expansion and technology enhancement alone are not enough to cope with the competition-driven scheduling practices of the airline industry. The policy legacy needs to change to be consistent with airport capacities. Flights on US airlines arrived late more often in the first four months of 2007 than in any other year since the government began tracking delays, and flight cancellations increased 91% over 2006. With a forecast of 1.1 billion yearly air travelers within the US by 2015, airport expansion and technology enhancement alone are not enough to cope with the competition-driven scheduling practices of the airline industry. Our research studies how flight schedules might change if airlines were required to restrict their schedules to runway capacity. To obtain these schedules, we model a profit-seeking, single benevolent airline whose goal is to maintain current competitive prices and service as many current passengers as possible, while remaining profitable. Our case study demonstrates that at Instrument Meteorological Conditions (IMC) runway rates, the market can find profitable flight schedules that reduce substantially the average flight delay to less than 6 minutes while simultaneously satisfying virtually all of the current demand with average prices remaining unchanged. This is accomplished through significant upgauging to high-demand markets. [ABSTRACT FROM AUTHOR]