Flight delays are frequent all over the world (about 20p of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2p and 71.8p recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8p and the delay recall is 86.9p. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.

Using Scalable Data Mining for Predicting Flight Delays

The consistently growing demand for airline transportation has resulted in increased air traffic and air operations in airports across the world. According to the International Air Transport Association, forecasts assume that in the year 2036 about 7.8 billion passengers will travel using air transport. In Europe, it is estimated that 16.2 million flights will take place in 2040, 53% more than in 2017. One of the crucial factors that significantly affect air transportation is the weather. In this article, ERA5 reanalysis and a 40-year timeframe (1979–2018) are used to construct the European climatology of environmental proxies associated with hazardous weather conditions causing disruptions in airline traffic. These include situations with limited visibility, thunderstorm, low-level wind shear, and snowfall. Results indicate that northern Europe and mountain zones are the most often affected by unfavorable weather conditions, while southern Europe is the least affected. Winter has the highest estimated number of situations with low-level wind shear and snowfall conditions. Situations with limited visibility are the most frequent in autumn and winter while thunderstorms are the main threat during summer over continental Europe and autumn in the Mediterranean area. Long-term changes of the analyzed proxies indicate that situations with low-level wind shear and snowfall are becoming less frequent while the number of thunderstorms generally increases, particularly over northern, central and south-central Europe. A decrease of situations with limited visibility is observed over majority of Europe with the exception of Scandinavia and parts of central Europe.

Hazardous weather affecting European airports: Climatological estimates of situations with limited visibility, thunderstorm, low-level wind shear and snowfall from ERA5

We model the impact of weather and arrival demand on ground delay program (GDP) incidence. We use Support Vector Machine (SVM) to analyze how regional convective weather affects GDP incidence and find the impact depends on both distance and direction of convective activity from the airport. We then train and compare the performance of logistic regression (LR) and random forest (RF) in predicting GDP incidence using an SVM-generated regional weather variable, local weather and arrival demand. Generally, RF outperforms LR. Convective weather is the most important factor in predicting GDP incidence at Atlanta International Airport (ATL), while arrival demand has greater impact for the other airports studied. We also examined model transferability across different airports. Lastly, we build GDP duration prediction models to enable a user to assess how long a GDP is likely to continue, if it is in effect in a given hour.

Using machine learning to analyze air traffic management actions: Ground delay program case study

Historical data are used to build two types of models that predict Ground Delay Program implementation decisions and produce insights into how and why those decisions are made. More specifically, b...

/pdf/ground-delay-program-analytics-with-behavioral-cloning-and-1t730jnmgz.pdf

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours.

/pdf/ground-delay-program-analytics-with-behavioral-cloning-and-30c3ab34g7.pdf

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

https://ntrs.nasa.gov/api/citations/20110018181/downloads/20110018181.pdf

Modeling Weather Impact on Ground Delay Programs

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been currently provided under current day operations. Traffic flow managers can use these predictions to identify potential MIT restrictions to eliminate (e.g., those occurring during low arrival demand periods), and to determine the days in which a significant number of restrictions may be required

/pdf/modeling-weather-impact-on-airport-arrival-miles-in-trail-3po73sjssh.pdf

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

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Modeling Weather Impact on Ground Delay Programs

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions