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.

Public bus transit operates within an intricate network of routes and stops, where delays are common and can propagate throughout the transit system, affecting systemreliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation has primarily focused on route-level delays correlation-basedanalysis, lacking a comprehensive understanding of underlying causal mechanisms of bus delay propagation from a network-level perspective. To enhance our understanding of bus delay propagation within urban transit systems, this study aims to develop a new approach that captures the causal relationshipsbetween stop delays, integrating their temporal and spatial characteristics. Utilizing a causal discovery algorithm for time series data, the thesis infers causal relationshipsfrom bus operation time series data. It then analyze the resulting causal graphs based on complex network measurement indicators. A case study using GTFS data of Stockholm, Sweden, was conducted. The results reveal that stops with a high degree of connections significantly influence delay propagation, with the network exhibiting a community structure that includes both high-degree and low-degree stops. Stops are classified based on their levels into four distinct delay propagation patterns. Critical stops are identified as either delay aggravation or absorption stops, based on their Momentary Conditional Independence (MCI) values. A new metric was constructed, underscoring the importance of considering delays across the entire network rather than isolating analysis to individual routes. The comparison with traditional correlation-based analysis highlights instances of low correlation among stops with high causality and high correlations without underlying causality, emphasizing the deeper insight from the causal approach

Airline delays lead to a tremendous loss of time and resources and cost billions of dollars every year in the United States (U.S.). At certain times, individual airports become bottlenecks within the National Airspace System (NAS). To explore solutions for reducing the delay, it is essential to understand factors causing flight delay and its impact on airports in the NAS. Major causal factors of flight delay at airports include over-scheduling, en-route convective weather, reduced ceiling and visibility around airports, and upstream delay propagation. Delay at one airport can be passed on to other airports in the NAS, in another word, operational improvement at one airport will have network effect and benefit to other airports as well. Moreover delay at different airports in a region might agglomerate to cause delay at different regions in the NAS. Hence, to optimally allocate NAS resources, e.g. capital investment for airport capacity expansion, the impact of single airport delay to the NAS and vice versa need to be investigated and quantified. For air transportation planning and policy purposes, this study concentrates on providing answers from a macroscopic point of view without being distracted by volatile operational details. In the first part, we estimate the interaction between flight delay at one single airport and delay at the rest of the NAS (RNAS) using case study for LaGuardia (LGA) and Chicago O'Hare (ORD) airports. In the second part, this research applies multivariate simultaneous regression models to quantify airport delay spillover effects across 34 of the 35 Operational Evolution Plan (OEP) airports and the RNAS. Observing the interactions between these two models, they are regressed with an econometric technique; three stage least square (3SLS). Thus, the regression results help us to determine the delay interactions between different airports and the RNAS and compare these airports based on delay propagation characteristics. Another significant contribution of this research is that, the estimated coefficients can be used for determining the marginal effects of all the delay causal factors presented in the model. Also, regional airport system development has been a hot topic of research in the air transportation community in recent years. Many metropolitan regions are served with more than one airport making their operations synchronized and interdependent and are known as regional airport system. This paper studies nine different prospective regions with multi-airport systems in the U.S. and identifies various key factors affecting the delay in these regions. Econometrics models and three stage least square (3SLS) estimation method are used to explore interdependency of delay at the multi-airport system and the RNAS. Along with it, different factors affecting delay at the system and the RNAS is being identified from the research. The outcomes from this research will help aviation planners understand the spillover effects of delays from multi-airport systems and provide decision support for future NAS improvement.

This thesis shows the advantages of simple models for analysis of railway operation. It presents two tools for infrastructure and timetable planning. It shows how the infrastructure can be analysed through fictive line designs, how the timetable can be treated as a variable and how delays can be used as performance measures. The thesis also gives examples of analyses of complex traffic situations through simulation experiments. Infrastructure configuration, timetable design and delays play important roles in the competitiveness of railway transportation. This is especially true on single-track lines where the run times and other timetable related parameters are severely restricted by crossings (train meetings). The first half of this thesis focuses on the crossing time, i.e. the time loss that occurs in crossing situations. A simplified analytical model, SAMFOST, has been developed to calculate the crossing time as a function of infrastructure configuration, vehicle properties, timetable and delays for two crossing trains. Three measures of timetable flexibility are proposed and they can be used to evaluate how infrastructure configuration, vehicle properties, punctuality etc affect possibilities to alter the timetable. Double-track lines operated with mixed traffic show properties similar to those of single-tracks. In this case overtakings imply scheduled delays as well as risk of delay propagation. Two different methods are applied for analysis of double-tracks: a combinatorial, mathematical model (TVEM) and simulation experiments. TVEM, Timetable Variant Evaluation Model, is a generic model that systematically generates and evaluates timetable variants. This method is especially useful for mixed traffic operation where the impact of the timetable is considerable. TVEM may also be used for evaluation of different infrastructure designs. Analyses performed in TVEM show that the impact on capacity from the infrastructure increases with speed differences and frequency of service for the passenger trains, whereas the impact of the timetable is strongest when the speed differences are low and/or the frequency of passenger services is low. Simulation experiments were performed to take delays and perturbations into account. A simulation model was set up in the micro simulation tool RailSys and calibrated against real operational data. The calibrated model was used for multi-factor analysis through experiments where infrastructure, timetable and perturbation factors were varied according to an experimental design and evaluated through response surface methods. The additional delay was used as response variable. Timetable factors, such as frequency of high-speed services and freight train speed, turned out to be of great importance for the additional delay, whereas some of the perturbation factors, i.e. entry delays, only showed a minor impact. The infrastructure factor, distance between overtaking stations, showed complex relationships with several interactions, principally with timetable factors. / QC20100622 / Framtida infrastruktur och kvalitet i tågföring

Ortsbasierte Dienste erfreuen sich in den letzten Jahren starker Beliebtheit. Für deren Umsetzung sind sog. Lokalisierungsdienste notwendig, welche eine Ortung von Mobilgeräten erlauben. Das GPS stellt den wohl populärsten Lokalisierungsdienst dar, ist jedoch innerhalb von Gebäuden nur sehr beschränkt einsetzbar. In der Vergangenheit wurden deshalb Methoden vorgeschlagen, die zur Positionsbestimmung auf die Messung der Empfangsfeldstärke von WLAN-Aussendungen zurückgreifen. Im Rahmen der Diplomarbeit soll untersucht werden, ob sich ebenfalls eine Postionsbestimmung anhand von Laufzeiten der WLAN-Signale umsetzen lässt. Bedingung hierbei ist, dass der Lokalisierungsdienst - eine reine Softwarelösung darstellt und keine Modifikationen an Hard- oder Firmware voraussetzt und - die Lokalisierung ohne für diese Zwecke ausgelegte Spezialhardware umsetzbar ist. Diese Anforderungen sollen sicherstellen, dass der zu entwickelnde Lokalisierungsdienst mit bereits installierter, handelsübliche Hardware umsetzbar ist. Es sind in Frage kommende Verfahren zur Bestimmung der Signallaufzeit zu erörtern. Für die Laufzeitmessung in Frage kommenden Zeitquellen sollen zugänglich gemacht und auf ihre Tauglichkeit untersucht werden. Durch Messreihen ist zu untersuchen, ob mit den vorgeschlagenen Messverfahren eine Lokalisierung möglich ist und in welchem Rahmen sich die zu erwartende Genauigkeit bewegt. Die in dieser Arbeit beschriebenen Konzepte sollen im Rahmen einer Proof of Concept Anwendung implementiert werden. Die Software soll unter dem Gesichtspunkt der Wiederverwendbarkeit entwickelt werden, um eine spätere Nutzung im Rahmen anderer Projekte zu ermöglichen.

We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ‘hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections.