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ANALYSIS OF AIRCRAFT ARRIVAL DELAY AND AIRPORT ON-TIME PERFORMANCE
Title
ANALYSIS OF AIRCRAFT ARRIVAL DELAY AND AIRPORT ON-TIME PERFORMANCE.
Creator
Bai, Yuqiong, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description
In this research, statistical models of airport delay and single flight arrival delay were developed. The models use the Airline On-Time Performance Data from the Federal Aviation Administration (FAA) and the Surface Airways Weather Data from the National Climatic Data Center (NCDC). Multivariate regression, ANOVA, neural networks and logistic regression were used to detect the pattern of airport delay, aircraft arrival delay and schedule performance. These models are then integrated in the...
Show moreIn this research, statistical models of airport delay and single flight arrival delay were developed. The models use the Airline On-Time Performance Data from the Federal Aviation Administration (FAA) and the Surface Airways Weather Data from the National Climatic Data Center (NCDC). Multivariate regression, ANOVA, neural networks and logistic regression were used to detect the pattern of airport delay, aircraft arrival delay and schedule performance. These models are then integrated in the form of a system for aircraft delay analysis and airport delay assessment. The assessment of an airport¡¯s schedule performance is discussed. The results of the research show that the daily average arrival delay at Orlando International Airport (MCO) is highly related to the departure delay at other airports. The daily average arrival delay can also be used to evaluate the delay performance at MCO. The daily average arrival delay at MCO is found to show seasonal and weekly patterns, which is related to the schedule performance. The precipitation and wind speed are also found contributors to the arrival delay. The capacity of the airport is not found to be significant. This may indicate that the capacity constraint is not an important problem at MCO. This research also investigated the delays at the flight level, including the flights with delay ¡Ý0 minute and the flights with delay ¡Ý15min, which provide the delay pattern of single arrival flights. The characteristics of single flight and their effect on flight delay are considered. The precipitation, flight distance, season, weekday, arrival time and the time spacing between two successive arriving flights are found to contribute to the arrival delay. We measure the time interval of two consecutive flights spacing and analyze its effect on the flight delay and find that for a positively delayed flight, as the time space increases, the probability of the flights being delayed will decrease. While it was possible to calculate the immediate impact of originating delays, it is not possible to calculate their impact on the cumulative delay. If a late departing aircraft has no empty space in its down line schedule, it will continue to be late. If that aircraft enters a connecting airport, it can pass its lateness on to another aircraft. In the research we also consider purifying only the arrival delay at MCO, excluding the flights with originating delay >0. The model makes it possible to identify the pattern of the aircraft arrival delay. The weather conditions are found to be the most significant factors that influence the arrival delay due to the destination airport.
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Date Issued
2006
Identifier
CFE0001049, ucf:46808
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001049
IMPROVING AIRLINE SCHEDULE RELIABILITY USING A STRATEGIC MULTI-OBJECTIVE RUNWAY SLOT ASSIGNMENT SEARCH HEURISTIC
Title
IMPROVING AIRLINE SCHEDULE RELIABILITY USING A STRATEGIC MULTI-OBJECTIVE RUNWAY SLOT ASSIGNMENT SEARCH HEURISTIC.
Creator
Hafner, Florian, Sepulveda, Alejandro, University of Central Florida
Abstract / Description
Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the...
Show moreImproving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.
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Date Issued
2008
Identifier
CFE0002067, ucf:47572
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002067