In order to meet the fast-growing demand, airlines have applied much more compact air-fleet operation schedules which directly lead to airport congestion. One result is the flight delay, which appears more frequently and seriously; the flight delay can also significantly damage airline's profitability and reputation The aim of this project is to enhance the dispatch reliability of Australian X Airline's fleet through a newly developed approach to reliability modeling, which employs computer-aided numerical simulation of the departure delay distribution and related cost to achieve the flight schedule optimization. The reliability modeling approach developed in this project is based on the probability distributions and Monte Carlo Simulation (MCS) techniques. Initial (type I) delay and propagated (type II) delay are adopted as the criterion for data classification and analysis. The randomicity of type I delay occurrence and the internal relationship between type II delay and changed flight schedule are considered as the core factors in this new approach of reliability modeling, which compared to the conventional assessment methodologies, is proved to be more accurate on the departure delay and cost evaluation modeling. The Flight Delay and Cost Simulation Program (FDCSP) has been developed (Visual Basic 6.0) to perform the complicated numerical calculations through significant amount of pseudo-samples. FDCSP is also designed to provide convenience for varied applications in dispatch reliability modeling. The end-users can be airlines, airports and aviation authorities, etc. As a result, through this project, a 16.87% reduction in departure delay is estimated to be achieved by Australian X Airline. The air-fleet dispatch reliability has been enhanced to a higher level - 78.94% compared to initial 65.25%. Thus, 13.35% of system cost can be saved. At last, this project also achieves to set a more practical guideline for air-fleet database and management upon overall dispatch reliability optimization.

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 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.

This dissertation looks at the relationship between airport size (e.g. small, medium, large) and air service continuity and operational performance. It consists of three studies, each written in journal format. The first study analyzes the markets served pre- and post-recession while focusing on the operational strategies adopted by the top Major Carriers and Low-Cost Carriers (LCCs) in the United States. Findings show that LCCs have outpaced major carriers in terms of expanding their network and the number of markets served. During the same time, major carriers have gained a greater flight share in the markets they already serve. Post-recession, LCCs have shown preference to competing with major carriers over other LCCs. The second study investigates the declining service levels at small airports compared to large-hub airports, which continue to benefit from higher levels of service and increased airline presence. Using a fixed-effects conditional logistic regression, this study looked at factors contributing to service loss in region-to-region markets serving small communities between 2007 and 2013. Results show that 1) markets affected by a merger are indeed at a higher risk of losing service; 2) markets that are operated by a fuel-intensive, small-aircraft fleet have a higher chance to be discontinued and 3) an increased number of competitors greatly reduces potential market service loss. The third and final study proposes a new methodology to calculate original delay and propagated delays using combined aviation operational datasets that provide detailed flight information and causal factors behind delays. In addition to calculating original and propagated delay for the month of July of 2018, this study differentiated between original delays that occur during the turnaround phase, taxiing phase and en-route and incorporates causal factor information to identify the true source behind propagated delay. Two fixed-effects linear regression models were introduced that predict Total Propagated Delay and the share of propagated delay given an airport's ability to absorb upstream delay during the turnaround phase. Results show that most delay propagation chains originate at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover (i.e. least number of downstream flight legs affected) and large-hub airports have a higher ability to absorb delay at the turnaround phase compared to smaller airports given the significantly higher schedule buffer time airlines plan at large-hub airports. / Doctor of Philosophy / The changing nature of the air service industry is dependent on several key factors, including but not limited to the major and low-cost airlines, the frequency of service at different sized-airports and the operational performance of the airports in the system. Each airport can be classified by size based on the annual number of enplanements. This dissertation looks at the relationship between airport size (e.g. small, medium, large), service continuity and operational performance. It consists of three studies, each written in journal format. Over the past two decades, the U.S. air transportation network witnessed several economic downturns forcing airlines to shift their operational strategies, cease service or merge with an airline counterpart. The first study analyzes routes served before and after the recession by exploring the presence of major and low-cost carriers in these markets to understand how several economic downturns have influenced the operating strategy of airlines in the US. While Low-cost carriers focused on expanding their network and offering service in an increased number of new routes, major carriers increased their presence in the markets in which they already serve. Furthermore, after the recession, low-cost carriers chose to increasingly compete with major carriers over their low-cost counterparts. The second study explored the factors that can potentially contribute to the loss of service in routes serving small communities. While airlines continue to compete on the most profitable routes, small airports recently suffered from reduced service levels and in some instance service discontinuity. Results show that 1) routes that were once served by two airlines that merged are at a higher risk of losing service; 2) routes that are operated by a fuel-intensive small aircraft fleet have a higher chance to be discontinued and 3) an increased presence of airlines competing in a route greatly reduces potential service loss. In addition to evaluating service continuity, the third and final study looks at flight delays across the US and dives into the effect of airport size on propagated delay. Delays on a flight can be caused by inefficiencies and capacity restrictions at airports and may also be the result of delay that happen earlier in the day and that propagates to multiple flights downstream that share the same resources. That is, a delay can affect multiple flights whenever these flights are all operated by the same aircraft equipment. Costing the air transportation network billions of dollars annually, the third study examines the original and propagated delays at US airports by collecting data from multiple sources to incorporate the original source and cause of delay. Results show that most delay originates at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover and large-hub airports have a higher ability to absorb delay at the turn compared to smaller airports as airlines allocate additional minutes of schedule padding at large-hub airports.

桃園機場近三年來，航班延誤的次數居高不下。 本文通過回歸和資料探勘的研究方法，發現抵達航班更容易出現航班延誤的狀況，特別是從桃園機場附近起飛的航班。 另外，廉價航空公司因其高效率的商業模式而獲得更好的時間效能。 這項研究證實了網絡傳播時間效應，也就是延誤狀況經常在當天晚些時候發生，並且進一步表明，機場若要提高準時性能，則航空公司的營運方式需要做改變。 / Over the past three years, Taoyuan Airport has experienced a high number of flight delays. By using regression and data mining methods, this paper found that arrival flights are more prone to delays, particularly from airports close by. Low cost carriers are also found to have better on time performance due to their efficient business. This study confirms the network propagation time effect where delays are more prone to happen later in the day and furthermore shows that should airports improve on-time performance, it would need to affect the way airlines operate.

The thesis is focused on dynamically developing field of the air transportation. It is based on rights and duties of airlines and consumers. It is mapping the most important current law moves of Czech Republic which has been changed and agreed for a consumer protection. It describes consumer rights in the case of a flight delay and cancellation, refusal of entry on the plane and in the case of baggage problems. It also touches the issues of advertised price of plane tickets. It is followed by the analysis of current situation of the observance of consumer rights and their point of view on some controversial topics.