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@techreport{sternberg_experimental_2016,
	title = {Experimental {Evaluation}},
	institution = {https://sourceforge.net/p/gpca/wiki/FlightDelayAssociationRules//},
	author = {Sternberg, Alice and Carvalho, Diego and Murta, Leonardo and Soares, Jorge and Ogasawara, Eduardo},
	year = {2016},
	note = {}
}

@Article{abdel-aty_detecting_2007,
  Title                    = {Detecting periodic patterns of arrival delay},
  Author                   = {Abdel-Aty, Mohamed and Lee, Chris and Bai, Yuqiong and Li, Xin and Michalak, Martin},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2007},
  Note                     = {00013},
  Number                   = {6},
  Pages                    = {355 -- 361},
  Volume                   = {13},

  Abstract                 = {This study identifies the periodic patterns of arrival delay for non-stop domestic flights at the Orlando International Airport during 2002â€“2003. Cyclic variations in air travel demand and weather at the airport were observed and their consequent effects on flight delay were investigated. This study detected the frequencies of any regularly repeating delay patterns and then identified the factors associated with the detected frequencies of delay. These sequential tasks called the â€œtwo-stage approachâ€? were performed using a mathematical frequency analysis and statistical analysis techniques. The results of the frequency analysis showed that arrival delay displayed daily, weekly and seasonal patterns of variation. The results of the statistical analysis showed that time of day, day of week, season, flight distance, precipitation at \{MCO\} and scheduled time intervals between successive flights were significantly correlated with arrival delay.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2007.06.002},
  ISSN                     = {0969-6997},
  Keywords                 = {Flight delays},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699707000646}
}

@Article{abdelghany_model_2004,
  Title                    = {A model for projecting flight delays during irregular operation conditions},
  Author                   = {Abdelghany, Khaled F. and Shah, Sharmila S. and Raina, Sidhartha and Abdelghany, Ahmed F.},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2004},
  Note                     = {00057},
  Number                   = {6},
  Pages                    = {385 -- 394},
  Volume                   = {10},

  Abstract                 = {On-time performance of airlines schedule is key factor in maintaining current customer satisfaction and attracting new ones. This requires management of the different operation resources (crew/aircraft) to ensure their on-time readiness for each flight in the planned schedule. However, flight schedules are often subjected to irregularity. In particular, weather accounts for nearly 75\% of system delays. Due to the tight connection among airlines resources, these delays could dramatically propagate over time and space unless the proper recovery actions are taken. This paper presents a model which projects flight delays and alerts for possible future breaks during irregular operation conditions. The results of applying the model at the operation control center of a major airlines company in the \{US\} are presented.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2004.06.008},
  ISSN                     = {0969-6997},
  Keywords                 = {Airlines schedule},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699704000377}
}

@InProceedings{agrawal_fast_1994,
  Title                    = {Fast algorithms for mining association rules},
  Author                   = {Agrawal, Rakesh and Srikant, Ramakrishnan and {others}},
  Booktitle                = {Proc. 20th int. conf. very large data bases, {VLDB}},
  Year                     = {1994},
  Note                     = {18611},
  Pages                    = {487--499},
  Volume                   = {1215}
}

@Article{ahmadbeygi_analysis_2008,
  Title                    = {Analysis of the potential for delay propagation in passenger airline networks},
  Author                   = {AhmadBeygi, Shervin and Cohn, Amy and Guan, Yihan and Belobaba, Peter},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2008},
  Note                     = {00084},
  Number                   = {5},
  Pages                    = {221 -- 236},
  Volume                   = {14},

  Abstract                 = {The paper analyzes the potential for delays to propagate in passenger airline networks. The aim is to better understand the relationship between the scheduling of aircraft and crew, and the operational performance of such schedules. In particular, when carriers decide how to schedule costly resources, the focus is primarily on achieving high levels of utilization. The resulting plans, however, often have little slack, limiting the schedule's ability to absorb disruption; instead, initial flight delays may propagate to delay subsequent flights as well. Understanding the relationship between planned schedules and delay propagation is a requisite precursor to developing tools for building more robust airline plans. This relationship is investigated using the flight data provided by two major \{US\} carriers, one traditional hub-and-spoke and one low-fare carrier operating a predominantly point-to-point network.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2008.04.010},
  ISSN                     = {0969-6997},
  Keywords                 = {Planning},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699708000550}
}

@TechReport{anac_agencia_2015,
  Title                    = {Agência {Nacional} de {Aviação} {Civil}},
  Author                   = {{ANAC}},
  Institution              = {http://www.anac.gov.br/},
  Year                     = {2015},
  Note                     = {00000},

  Urldate                  = {2014-08-20}
}

@TechReport{anac_anuario_2014,
  Title                    = {Anuário {Estatístico} do {Transporte} 2013},
  Author                   = {{ANAC}},
  Institution              = {http://www2.anac.gov.br/estatistica/anuarios.asp},
  Year                     = {2014},
  Note                     = {00000},

  Urldate                  = {2014-08-20}
}

@techreport{anac_anuario_2015,
	title = {Anuário {Estatístico} do {Transporte} 2014},
	urldate = {2016-01-28},
	institution = {http://www2.anac.gov.br/estatistica/anuarios.asp},
	author = {{ANAC}},
	year = {2015},
	note = {}
}

@Article{balakrishna_accuracy_2010,
  Title                    = {Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: {A} case-study of {Tampa} {Bay} departures},
  Author                   = {Balakrishna, Poornima and Ganesan, Rajesh and Sherry, Lance},
  Journal                  = {Transportation Research Part C: Emerging Technologies},
  Year                     = {2010},
  Note                     = {00017},
  Number                   = {6},
  Pages                    = {950 -- 962},
  Volume                   = {18},

  Abstract                 = {Taxi-out delay is a significant portion of the block time of a flight. Uncertainty in taxi-out times reduces predictability of arrival times at the destination. This in turn results in inefficient use of airline resources such as aircraft, crew, and ground personnel. Taxi-out time prediction is also a first step in enabling schedule modifications that would help mitigate congestion and reduce emissions. The dynamically changing operation at the airport makes it difficult to accurately predict taxi-out time. In this paper we investigate the accuracy of taxi out time prediction using a nonparametric reinforcement learning (RL) based method, set in the probabilistic framework of stochastic dynamic programming. A case-study of Tampa International Airport (TPA) shows that on an average, with 93.7\% probability, on any given day, our predicted mean taxi-out time for any given quarter, matches the actual mean taxi-out time for the same quarter with a standard error of 1.5\&\#xa0;min. Also, for individual flights, the taxi-out time of 81\% of them were predicted accurately within a standard error of 2\&\#xa0;min. The predictions were done 15\&\#xa0;min before gate departure. Gate OUT, wheels OFF, wheels ON, and gate \{IN\} (OOOI) data available in the Aviation System Performance Metric (ASPM) database maintained by the Federal Aviation Administration (FAA) was used to model and analyze the problem. The prediction accuracy is high even without the use of detailed track data.},
  Doi                      = {http://dx.doi.org/10.1016/j.trc.2010.03.003},
  ISSN                     = {0968-090X},
  Keywords                 = {Taxi-out prediction},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0968090X1000029X}
}

@InCollection{ball_chapter_2007,
  Title                    = {Chapter 1 {Air} {Transportation}: {Irregular} {Operations} and {Control}},
  Author                   = {Ball, Michael and Barnhart, Cynthia and Nemhauser, George and Odoni, Amedeo},
  Booktitle                = {Transportation},
  Publisher                = {Elsevier},
  Year                     = {2007},
  Editor                   = {Barnhart, Cynthia and Laporte, Gilbert},
  Note                     = {00000},
  Pages                    = {1 -- 67},
  Series                   = {Handbooks in {Operations} {Research} and {Management} {Science}},
  Volume                   = {14},

  Abstract                 = {Publisher Summary This chapter discusses problems related to the management of air traffic and airline operations for minimizing the impact and cost of disruptions. Flight and crew schedules and passenger itineraries have become increasingly fragile because of the growing complexity of the air transportation system and the tight coupling of its various elements. The resulting direct and indirect economic costs are very large, certainly amounting to several billion dollars annually. The airline industry has a vital stake in research aimed at mitigating the effects of severe weather and other disruptive events and at expediting recovery from irregular operations. A significant body of recent and ongoing work has led to major progress toward these objectives. Two breakthrough developments have been the primary drivers behind this progress. First, collaborative decision making has made it possible to apply the principles of information sharing and distributed decision making to air traffic flow management (ATFM) by expanding the databases available to airline and Federal Aviation Administration (FAA) traffic flow managers, by creating common situational awareness, and by introducing shared real-time tools and procedures. Second, there is growing recognition in the airline industry of the fact that planning for schedule robustness and reliability may be just as important as planning for minimizing costs in the complex, highly stochastic, and dynamic environment of air transportation.},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0927050706140013}
}

@Article{beatty_preliminary_1998,
  Title                    = {Preliminary evaluation of flight delay propagation through an airline schedule},
  Author                   = {Beatty, Roger and Hsu, Rose and Berry, Lee and Rome, James},
  Journal                  = {2nd USA/EUROPE AIR TRAFFIC MANAGEMENT R\&D SEMINAR},
  Year                     = {1998},
  Note                     = {00116},
  Number                   = {4},
  Pages                    = {259--270},
  Volume                   = {7}
}

@Article{bhadra_you_2009,
  Title                    = {You (expect to) get what you pay for: {A} system approach to delay, fare, and complaints},
  Author                   = {Bhadra, Dipasis},
  Journal                  = {Transportation Research Part A: Policy and Practice},
  Year                     = {2009},
  Note                     = {00009},
  Number                   = {9‚Äì10},
  Pages                    = {829 -- 843},
  Volume                   = {43},

  Abstract                 = {In this paper, an analytical framework integrating delay, fare, and complaints with passenger air travel has been laid out. Examining aggregate monthly data for \{US\} domestic air travel, we have identified causal relationships among fare, complaints, and levels of delay. An analytical framework is proposed that formalizes these relationships in an integrated manner. This integrated framework is then estimated in a set of simultaneous equations by using 118\&\#xa0;months of data from January 1997 to October 2006. Results show that complaints are influenced by levels of delays. However, complaints are positively influenced by average yield. These findings lead us to support the central hypothesis that complaints are responsive to levels of delays, but they tend to vary according to fare. That is, air travelers are less likely to complain in return for lower fares, even when faced with the same or even higher levels of delays. These findings have important policy implications, including the passengers‚Äô bill of rights and regulator‚Äôs choice between market and operational performances.},
  Doi                      = {http://dx.doi.org/10.1016/j.tra.2009.08.003},
  ISSN                     = {0965-8564},
  Keywords                 = {Air transportation},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0965856409000962}
}

@Book{boswell_analysis_1997,
  Title                    = {Analysis of downstream impacts of air traffic delay},
  Author                   = {Boswell, Steven B and Evans, James Everett},
  Publisher                = {Lincoln Laboratory, Massachusetts Institute of Technology},
  Year                     = {1997},
  Note                     = {00021}
}

@Book{bowman_r_2014,
  Title                    = {R package {\textbackslash}textttsm: nonparametric smoothing methods (version 2.2-5.4)},
  Author                   = {Bowman, A. W. and Azzalini, A.},
  Year                     = {2014},

  Address                  = {University of Glasgow, UK and Università di Padova, Italia},
  Note                     = {00000},

  Url                      = {URL http://www.stats.gla.ac.uk/ adrian/sm, http://azzalini.stat.unipd.it/Book_sm}
}

@Article{britto_impact_2012,
  Title                    = {The impact of flight delays on passenger demand and societal welfare},
  Author                   = {Britto, Rodrigo and Dresner, Martin and Voltes, Augusto},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2012},
  Note                     = {00010},
  Number                   = {2},
  Pages                    = {460 -- 469},
  Volume                   = {48},

  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.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2011.10.009},
  ISSN                     = {1366-5545},
  Keywords                 = {Flight delays},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S1366554511001347}
}

@TechReport{bts_bureau_2015,
  Title                    = {The {Bureau} of {Transportation} {Statistics} databases},
  Author                   = {{BTS}},
  Institution              = {http://www.rita.dot.gov/bts/home},
  Year                     = {2015},
  Note                     = {00000},

  Urldate                  = {2014-08-20}
}

@book{burden_numerical_2015,
	title = {Numerical analysis},
	publisher = {Nelson Education},
	author = {Burden, Richard and Faires, J and Burden, Annette},
	year = {2015},
	note = {}
}

@InCollection{cule_mining_2011,
  Title                    = {Mining train delays},
  Author                   = {Cule, Boris and Goethals, Bart and Tassenoy, Sven and Verboven, Sabine},
  Booktitle                = {Advances in {Intelligent} {Data} {Analysis} {X}},
  Publisher                = {Springer},
  Year                     = {2011},
  Note                     = {00012},
  Pages                    = {113--124}
}

@Article{duck_increasing_2012,
  Title                    = {Increasing stability of crew and aircraft schedules},
  Author                   = {Dück, Viktor and Ionescub, Lucian and Kliewerb, Natalia and Suhla, Leena},
  Journal                  = {Transportation Research Part C: Emerging Technologies},
  Year                     = {2012},
  Note                     = {00020},
  Number                   = {1},
  Pages                    = {47 -- 61},
  Volume                   = {20},

  Abstract                 = {Stability describes the grade of the ability of a plan to remain feasible and cost efficient under variations of the operating environment without major modifications to the plan. In airline traffic delays often lead to additional delays, because of interdependencies between different resources such as ai rcraft, crews and airport facilities. In this paper we focus on reactionary delays that result from crews changing aircraft. Based on a stochastic model for delay propagation we propose an indicator for stability of airline crew and aircraft schedules. This indicator is used to generate more robust aircraft and crew schedules. An integrated formulation for the aircraft and crew scheduling problem results in a non-linear stochastic recourse function. We decompose this formulation into separate linear problems connected by the objective function. The decomposed stochastic problem may be solved using a heuristic iterative approach based on column generation and dynamic programming for the recourse functions. In order to evaluate the robustness of the generated schedules and to compare with a deterministic approach for robust scheduling we use an also presented simulation model.},
  Doi                      = {http://dx.doi.org/10.1016/j.trc.2011.02.009},
  ISSN                     = {0968-090X},
  Keywords                 = {Airline scheduling},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0968090X11000350}
}

@TechReport{dac-iac1223_instrucao_2000,
  Title                    = {Instrução de {Aviação} {Civil} 1223 - {Normas} para {Confecção} e {Aprovação} de {Horario} de {Transporte} - {HOTRAN}},
  Author                   = {{DAC-IAC1223}},
  Institution              = {Departamento de Aviação Civil},
  Year                     = {2000},
  Note                     = {00000}
}

@TechReport{dac-iac1504_instrucao_2000,
  Title                    = {Instrução de {Aviação} {Civil} 1504 - {Procedimentos} para o {Registro} de {Alterações} em {Voos} de {Empresas} de {Transporte} {Aéreo} {Regular}},
  Author                   = {{DAC-IAC1504}},
  Institution              = {Departamento de Aviação Civil},
  Year                     = {2000},
  Note                     = {00000}
}

@Article{dhar_data_2013,
  Title                    = {Data science and prediction},
  Author                   = {Dhar, Vasant},
  Journal                  = {Communications of the ACM},
  Year                     = {2013},
  Note                     = {00058},
  Number                   = {12},
  Pages                    = {64--73},
  Volume                   = {56}
}

@TechReport{dot_united_2015,
  Title                    = {The {United} {States} {Department} of {Transportation}},
  Author                   = {{DOT}},
  Institution              = {http://www.dot.gov/},
  Year                     = {2015},
  Note                     = {00000},

  Urldate                  = {2014-08-20}
}

@Book{elmasri_fundamentals_2015,
  Title                    = {Fundamentals of {Database} {Systems}},
  Author                   = {Elmasri, Ramez and Navathe, Shamkant B.},
  Publisher                = {Addison-Wesley},
  Year                     = {2015},
  Edition                  = {7 edition},
  Month                    = jun,
  Note                     = {06393},

  Abstract                 = {Clear explanations of theory and design, broad coverage of models and real systems, and an up-to-date introduction to modern database technologies result in a leading introduction to database systems. Intended for computer science majors, this text emphasizes math models, design issues, relational algebra, and relational calculus.},
  ISBN                     = {978-0-13-397077-7},
  Language                 = {English}
}

@TechReport{eurocontrol_coda_2014,
  Title                    = {{CODA} {Digest} - {Delays} to {Air} {Transport} in {Europe} 2013},
  Author                   = {{EUROCONTROL}},
  Institution              = {https://www.eurocontrol.int/sites/default/files/content/documents/official-documents/facts-and-figures/coda-reports/coda-digest-annual-2013.pdf},
  Year                     = {2014},
  Note                     = {00000}
}

@techreport{eurocontrol_annual_2015,
	title = {Annual {Network} {Operations} {Report} 2014},
	institution = {https://www.eurocontrol.int/sites/default/files/publication/ performance/2014\_annual/final/annual\_network\_operations\_report\_2014\_main\_report\_final\_edition.pdf},
	author = {{EUROCONTROL}},
	year = {2015}
}

@TechReport{faa_federal_2015,
  Title                    = {Federal {Aviation} {Administration}},
  Author                   = {{FAA}},
  Institution              = {http://www.faa.gov/},
  Year                     = {2015},
  Note                     = {00002},

  Urldate                  = {2014-08-20}
}

@InCollection{flier_mining_2009,
  Title                    = {Mining railway delay dependencies in large-scale real-world delay data},
  Author                   = {Flier, Holger and Gelashvili, Rati and Graffagnino, Thomas and Nunkesser, Marc},
  Booktitle                = {Robust and {Online} {Large}-{Scale} {Optimization}},
  Publisher                = {Springer},
  Year                     = {2009},
  Note                     = {00016},
  Pages                    = {354--368}
}

@Article{geurts_profiling_2003,
  Title                    = {Profiling of high-frequency accident locations by use of association rules},
  Author                   = {Geurts, Karolien and Wets, Geert and Brijs, Tom and Vanhoof, Koen},
  Journal                  = {Transportation Research Record: Journal of the Transportation Research Board},
  Year                     = {2003},
  Note                     = {00084},
  Number                   = {1840},
  Pages                    = {123--130}
}

@Book{hahsler_arules:_2015,
  Title                    = {arules: {Mining} {Association} {Rules} and {Frequent} {Itemsets}},
  Author                   = {Hahsler, Michael and Buchta, Christian and Gruen, Bettina and Hornik, Kurt},
  Year                     = {2015},
  Note                     = {00004},

  Url                      = {http://CRAN.R-project.org/package=arules}
}

@Article{han_frequent_2007,
  Title                    = {Frequent pattern mining: current status and future directions},
  Author                   = {Han, Jiawei and Cheng, Hong and Xin, Dong and Yan, Xifeng},
  Journal                  = {Data Mining and Knowledge Discovery},
  Year                     = {2007},
  Note                     = {00831},
  Number                   = {1},
  Pages                    = {55--86},
  Volume                   = {15}
}

@Book{han_data_2011,
  Title                    = {Data {Mining}: {Concepts} and {Techniques}, {Third} {Edition}},
  Author                   = {Han, Jiawei and Kamber, Micheline and Pei, Jian},
  Publisher                = {Morgan Kaufmann},
  Year                     = {2011},

  Address                  = {Waltham, Mass.},
  Edition                  = {3 edition},
  Month                    = jul,
  Note                     = {00000},

  Abstract                 = {The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it√¢¬Ä¬ôs still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition√¢¬Ä¬ôs publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today√¢¬Ä¬ôs most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data},
  ISBN                     = {978-0-12-381479-1},
  Language                 = {English},
  Shorttitle               = {Data {Mining}}
}

@InProceedings{han_mining_2000,
  Title                    = {Mining frequent patterns without candidate generation},
  Author                   = {Han, Jiawei and Pei, Jian and Yin, Yiwen},
  Booktitle                = {{ACM} {SIGMOD} {Record}},
  Year                     = {2000},
  Note                     = {06163},
  Pages                    = {1--12},
  Publisher                = {ACM},
  Volume                   = {29}
}

@Article{hansen_micro-level_2002,
  Title                    = {Micro-level analysis of airport delay externalities using deterministic queuing models: a case study},
  Author                   = {Hansen, Mark},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2002},
  Note                     = {00045},
  Number                   = {2},
  Pages                    = {73 -- 87},
  Volume                   = {8},

  Abstract                 = {We analyze runway delay externalities at Los Angeles International Airport (LAX) using a deterministic queuing model. The model allows us to estimate the delay impact of each specific arriving flight on each other specific arriving flight. We find that, despite being only moderately congested (average queuing delay only 4\&\#xa0;min per arriving flight), individual flights can generate as much as 3 aircraft-hours of external delay impact on other flights, with an average impact of 26 aircraft-minutes and 3400 seat-minutes. About 90 percent of this impact is external to the airline as well as the flight, a consequence of the lack of a dominant airline at LAX. We then compare the delay impact of each individual flight to its contribution to schedule convenience by determining the amount of ‚Äúschedule delay‚Äù that would result if the flight were eliminated and its passengers forced to use the previous flight flown by the same airline from the same origin. We find that a number of commuter flights serving high density, short-haul segments generate much more queuing delay than they save in schedule delay, with the ratio exceeding 10 in several cases. We argue that social welfare would increase if such flights were eliminated, upsizing others as necessary to accommodate the displaced loads.},
  Doi                      = {http://dx.doi.org/10.1016/S0969-6997(01)00045-X},
  ISSN                     = {0969-6997},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S096969970100045X}
}

@Article{hao_new_2014,
  Title                    = {New {York}, {New} {York}: {Two} ways of estimating the delay impact of {New} {York} airports},
  Author                   = {Hao, Lu and Hansen, Mark and Zhang, Yu and Post, Joseph},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2014},
  Note                     = {00002},
  Number                   = {0},
  Pages                    = {245 -- 260},
  Volume                   = {70},

  Abstract                 = {Abstract High arrival delay at major airports tends to propagate and generate secondary delay through the National Airspace System (NAS). In the United States, it is widely believed that the major culprits for delay throughout the \{NAS\} are the three New York commercial airports ‚Äì Newark (EWR), LaGuardia (LGA), and John F. Kennedy (JFK). Various estimates of the extent to which the New York airports impact the delay in the \{NAS\} have been reported over the years. Yet there is no thorough investigation into the mutual relationship between delays at New York and non-New York airports. In this paper, we take two different approaches to quantify the impact of the three New York airports on delay throughout the NAS. First, we estimate and apply an econometric model using a large historical dataset. The other model is the \{FAA\} \{SWAC\} model that simulates flights and tracks the daily performance of the system. The counterfactual scenarios in these two models are adjusted to be comparable to each other. There is disparity between the results of the two different models, suggesting the simulation model might not capture all the factors that cause arrival delay. Still both results conclude that the portion of delay in the system caused by New York airports is much less than publicized estimates. Combining econometric and simulation models to address questions of this nature appears to be a promising approach.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2014.07.004},
  ISSN                     = {1366-5545},
  Keywords                 = {New York airports},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S1366554514001252}
}

@InProceedings{hunter_advanced_2007,
  Title                    = {Advanced national airspace traffic flow management simulation experiments and vlidation},
  Author                   = {Hunter, G. and Boisvert, B. and Ramamoorthy, K.},
  Booktitle                = {Simulation {Conference}, 2007 {Winter}},
  Year                     = {2007},
  Month                    = dec,
  Note                     = {00007},
  Pages                    = {1261--1267},

  Abstract                 = {Traffic flow management in the National Airspace is an important problem in our air transportation system. We have developed ProbTFM, a traffic flow management evaluation platform and algorithmic solution. ProbTFM works with existing traffic flow management tools and provides probabilistic data modeling and decision making. ProbTFM forecasts airport and airspace capacity and demand; and airport, airspace, and route congestion. ProbTFM creates a list of high congestion, "critical" flights and recommends delays or reroutes for specific flights. ProbTFM can be used as an evaluation platform for advanced traffic flow management concepts, and to model today's National Airspace System. In this paper we report on validation results and how ProbTFM can be used to understand operational tradeoffs and inform policy decisions.},
  Doi                      = {10.1109/WSC.2007.4419730},
  Keywords                 = {Aerodynamics, Aircraft, airport capacity forecasting, airports, airspace capacity forecasting, air traffic, Air transportation, air transportation system, decision making, decision theory, Equations, forecasting theory, National Airspace System, policy decision making, probabilistic data modeling, probability, ProbTFM traffic flow management simulation experiment, route congestion, simulation, Stochastic processes, Traffic control, transportation, Uncertainty, Weather forecasting}
}

@InProceedings{hunter_evaluation_2007,
  Title                    = {Evaluation of the national airspace system aggregate performance sensitivity},
  Author                   = {Hunter, G. and Ramamoorthy, K.},
  Booktitle                = {Digital {Avionics} {Systems} {Conference}, 2007. {DASC} '07. {IEEE}/{AIAA} 26th},
  Year                     = {2007},
  Month                    = oct,
  Note                     = {00004},
  Pages                    = {1.E.1--1--1.E.1--13},

  Abstract                 = {This paper describes research involving advanced traffic flow management concepts and solutions in the national airspace system. We summarize our tools and methods, and describe NAS performance metrics and sensitivities. Our results indicate that in addition traffic volume and delay, congestion is an important NAS performance metric. Our results also show that NAS performance is highly sensitive to the degree of delay distribution in the TFM solution. NAS performance is also sensitive to the agility of the TFM system. Finally, NAS performance is only slightly sensitive to forecasting improvements of traffic loading and weather.},
  Doi                      = {10.1109/DASC.2007.4391838},
  Keywords                 = {Aggregates, airports, air traffic control, air transportation system, Delay, Measurement, NAS forecasting improvements, National Airspace System, system aggregate performance sensitivity, Throughput, Traffic control, traffic flow management, Trajectory, Uncertainty, Weather forecasting, Wind}
}

@techreport{icao_annual_2015,
	title = {Annual {Report} of the {Council} 2014},
	urldate = {2016-02-16},
	institution = {http://www.icao.int/annual-report-2014/Pages/default.aspx},
	author = {{ICAO}},
	year = {2015}
}

@TechReport{inmet_instituto_2015,
  Title                    = {Instituto {Nacional} de {Meteorologia}},
  Author                   = {{INMET}},
  Institution              = {http://www.inmet.gov.br/portal/},
  Year                     = {2015},
  Note                     = {00007},

  Urldate                  = {2014-08-20}
}

@Article{jagadish_big_2014,
  Title                    = {Big data and its technical challenges},
  Author                   = {Jagadish, HV and Gehrke, Johannes and Labrinidis, Alexandros and Papakonstantinou, Yannis and Patel, Jignesh M and Ramakrishnan, Raghu and Shahabi, Cyrus},
  Journal                  = {Communications of the ACM},
  Year                     = {2014},
  Note                     = {00037},
  Number                   = {7},
  Pages                    = {86--94},
  Volume                   = {57}
}

@InProceedings{jindal_spatiotemporal_2013,
  Title                    = {Spatiotemporal periodical pattern mining in traffic data},
  Author                   = {Jindal, Tanvi and Giridhar, Prasanna and Tang, Lu-An and Li, Jun and Han, Jiawei},
  Booktitle                = {Proceedings of the 2nd {ACM} {SIGKDD} {International} {Workshop} on {Urban} {Computing}},
  Year                     = {2013},
  Note                     = {00005},
  Pages                    = {11},
  Publisher                = {ACM}
}

@Article{kim_deconstructing_2013,
  Title                    = {Deconstructing delay: {A} non-parametric approach to analyzing delay changes in single server queuing systems},
  Author                   = {Kim, Amy and Hansen, Mark},
  Journal                  = {Transportation Research Part B: Methodological},
  Year                     = {2013},
  Note                     = {00004},
  Number                   = {0},
  Pages                    = {119 -- 133},
  Volume                   = {58},

  Abstract                 = {Abstract This paper introduces an empirically driven, non-parametric method to isolate and estimate the effects that changes in demand and changes in throughput have on delay â€“ in particular, arrival and departure flight delay at airport runways. Classic queuing concepts were used to develop a method by which an intermediate, or counterfactual, queuing scenario could be constructed, to isolate the delay effects due to shifts in demand and throughput. This method includes the development of a stochastic throughput function that is based entirely on data and has three key features. Firstly, the function relies on non-parametric, empirically-based probability distributions of throughput counts. Secondly, facility capacity needs not be explicitly defined, as it is implicitly included in the probability distributions of throughput. Thirdly, the throughput performance function preserves the effect of factors that cause capacity (and, therefore, throughput) to fluctuate over a given period. Temporal sequences of high, moderate, and low capacity are maintained between the observed and counterfactual scenarios. The method was applied to a case study of the three major New York area airports of LaGuardia (LGA), Newark Liberty (EWR), and John F. Kennedy (JFK), using operational data extracted from the Federal Aviation Administrationâ€™s (FAAâ€™s) Aviation System Performance Metrics (ASPM) database. The focus was on the peak summer travel seasons of 2006 and 2007, as these airports experienced record levels of delay in 2007. The results indicate that decreases in both demand and throughput were experienced at \{LGA\} and EWR, although the decreases in throughput had more significant effects on operational delays as they increased overall at these airports. At JFK, the increase in departure throughput was not sufficient to offset the increase in departure demands. For arrivals, demand increased and throughput decreased. These trends caused a significant growth in delay at \{JFK\} between 2006 and 2007.},
  Doi                      = {http://dx.doi.org/10.1016/j.trb.2013.10.001},
  ISSN                     = {0191-2615},
  Keywords                 = {Counterfactual delay scenario},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0191261513001744}
}

@article{kuok_mining_1998,
  title={Mining fuzzy association rules in databases},
  author={Kuok, Chan Man and Fu, Ada and Wong, Man Hon},
  journal={ACM Sigmod Record},
  volume={27},
  number={1},
  pages={41--46},
  year={1998},
  publisher={ACM}
}

@InProceedings{lu_new_2009,
  Title                    = {A new method for flight delays forecast based on the recommendation system},
  Author                   = {Lu, Zonglei and Wang, Jiandong and Xu, Tao},
  Booktitle                = {Computing, {Communication}, {Control}, and {Management}, 2009. {CCCM} 2009. {ISECS} {International} {Colloquium} on},
  Year                     = {2009},
  Month                    = aug,
  Note                     = {00001},
  Pages                    = {46--49},
  Volume                   = {1},

  Abstract                 = {A new method to forecast flight delays has been presented in this paper. This new method is based on content-based recommendation system. In the forecast model, the events ldquoflight delaysrdquo and airports have been mapped to users and items, respectively, which are the concepts in the recommendation system. According to the propagation of the delay, this new method alerts the target airport by monitoring the status of related airports. The observed status is compared with the history data in order to predict the seriousness of delay. Since the airborne hours between each two airports are usually more than an hour, this method could give the alarm at least 1 hour ahead. Besides, the method requires minimum online calculation, and therefore it guarantees that the delay forecast can be delivered in a quick and timely manner.},
  Doi                      = {10.1109/CCCM.2009.5268153},
  Keywords                 = {aerospace computing, airborne hours, airports, content-based recommendation system, data mining, Delay effects, delay forecast, delay propagation, delays, Delay systems, Educational institutions, flight delay, flight delay forecast, forecasting theory, information filters, Information science, minimum online calculation, Predictive models, Propagation delay, recommendation system, Space technology, target airport, Technology forecasting, Weather forecasting}
}

@InProceedings{lu_new_2008,
  Title                    = {A {New} {Method} to {Alarm} {Large} {Scale} of {Flights} {Delay} {Based} on {Machine} {Learning}},
  Author                   = {Lu, Zonglei and Wang, Jiandong and Zheng, Guansheng},
  Booktitle                = {Knowledge {Acquisition} and {Modeling}, 2008. {KAM} '08. {International} {Symposium} on},
  Year                     = {2008},
  Month                    = dec,
  Note                     = {00015},
  Pages                    = {589--592},

  Abstract                 = {A new method to alarm large scale of flight delays based on machine learning is presented in this paper. This new method first does unsupervised learning on the data of the flights collected from the airport. The standard of each class of delay can be gotten after the learning process. With these classes of delay, the supervised learning method can be used on the data so that the alarm model could be built. Comparing with the recent manual alarm standard, this model synthesizes more factors to do alarm. Since the recent delay standard is only related to the number of flights, which is helpful only in serious delay case, the new model performs will be more practical value than recent ones.},
  Doi                      = {10.1109/KAM.2008.18},
  Keywords                 = {aerospace computing, airports, Classification, Clustering, Delay effects, Educational institutions, Flight delays, flights data, Flights Delay, Information science, Knowledge acquisition, Large-scale systems, Learning systems, machine learning, Space technology, supervised learning method, unsupervised learning}
}

@InProceedings{lv_flight_2009,
  Title                    = {Flight {Delay} {Alarming} {Analysis} for an {Airport} {Based} on {Markov}},
  Author                   = {Lv, Xiaojie and Wang, Hong},
  Booktitle                = {Education {Technology} and {Computer} {Science}, 2009. {ETCS} '09. {First} {International} {Workshop} on},
  Year                     = {2009},
  Month                    = mar,
  Note                     = {00000},
  Pages                    = {685--688},
  Volume                   = {1},

  Abstract                 = {Flight delay is the most prominent problem in the field of an airport to circulate to manage in recent years. In order to make a comparative accurate forecast of the flight delay situation, this paper gives an improved Markov model by adding the system with a weight adjustment. By using the optimization method, we compute state transition probability matrix, analyze the data from the capital airport, and forecast the probability of the departure flights in all the delaying time section. The probability of flight delay is divided into five levels. For each level we make a forecast with one day data chosen from the airport. The warning method for airport flight delay forecasting makes use of time fragmenting technique by an interval of 20 minutes. Simulation experiment gives out error analysis. And the comparison with delay probability getting from the practical data indicates that the forecast effect is ideal. It has good application value in the practical application.},
  Doi                      = {10.1109/ETCS.2009.158},
  Keywords                 = {airport, airport flight delay forecasting, airports, alarming, Computer science, Delay effects, Economic forecasting, Educational technology, Error correction, flight delay, flight delay alarming analysis, forecast, forecasting theory, Markov chain, Markov process, Markov processes, matrix algebra, optimisation, optimization method, Optimization methods, Predictive models, probability, state transition probability matrix}
}

@Article{matsudaira_science_2015,
  Title                    = {The science of managing data science},
  Author                   = {Matsudaira, Kate},
  Journal                  = {Communications of the ACM},
  Year                     = {2015},

  Month                    = may,
  Note                     = {00000},
  Number                   = {6},
  Pages                    = {44--47},
  Volume                   = {58},

  Doi                      = {10.1145/2745390},
  ISSN                     = {00010782},
  Language                 = {en},
  Url                      = {http://dl.acm.org/citation.cfm?doid=2783419.2745390},
  Urldate                  = {2015-06-02}
}

@Article{mirabadi_application_2010,
  Title                    = {Application of association rules in {Iranian} {Railways} ({RAI}) accident data analysis},
  Author                   = {Mirabadi, Ahmad and Sharifian, Shabnam},
  Journal                  = {Safety science},
  Year                     = {2010},
  Note                     = {00020},
  Number                   = {10},
  Pages                    = {1427--1435},
  Volume                   = {48}
}

@Article{mitra_data_2002,
  Title                    = {Data mining in soft computing framework: a survey},
  Author                   = {Mitra, Sushmita and Pal, Sankar K and Mitra, Pabitra},
  Journal                  = {IEEE transactions on neural networks},
  Year                     = {2002},
  Note                     = {00615},
  Number                   = {1},
  Pages                    = {3--14},
  Volume                   = {13}
}

@Article{morrison_effect_2008,
  Title                    = {The effect of \{{FAA}\} expenditures on air travel delays},
  Author                   = {Morrison, Steven A. and Winston, Clifford},
  Journal                  = {Journal of Urban Economics},
  Year                     = {2008},
  Note                     = {00030},
  Number                   = {2},
  Pages                    = {669 -- 678},
  Volume                   = {63},

  Abstract                 = {The Federal Aviation Administration (FAA) seeks to prevent the nation's aviation system from becoming congested. To reduce delays, the \{FAA\} makes investments in air traffic control. We assess the efficacy of these investments by developing an empirical model of delays that is motivated by air traffic control operations. We find that \{FAA\} spending has reduced the costs of delays to travelers and operators but that the \{FAA\} could generate greater benefits if spending were increased and efficiently allocated toward airports that experience the greatest delays.},
  Doi                      = {http://dx.doi.org/10.1016/j.jue.2007.04.005},
  ISSN                     = {0094-1190},
  Keywords                 = {Air transportation},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S009411900700071X}
}

@InProceedings{mueller_analysis_2002,
  Title                    = {Analysis of aircraft arrival and departure delay characteristics},
  Author                   = {Mueller, Eric R and Chatterji, Gano B},
  Booktitle                = {{AIAA} aircraft technology, integration and operations ({ATIO}) conference},
  Year                     = {2002},
  Note                     = {00075}
}

@TechReport{NOAA2015,
  Title                    = {National {Oceanic} and {Atmospheric} {Administration}},
  Author                   = {{NOAA}},
  Institution              = {http://www.noaa.gov/},
  Year                     = {2015},
  Note                     = {00012},

  __markedentry            = {[Alice:]}
}

@Article{pai_factors_2010,
  Title                    = {On the factors that affect airline flight frequency and aircraft size},
  Author                   = {Pai, Vivek},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2010},
  Note                     = {00037},
  Number                   = {4},
  Pages                    = {169 -- 177},
  Volume                   = {16},

  Abstract                 = {This paper assesses the determinants of aircraft size and frequency of flights on \{US\} airline routes by considering market demographics, airport characteristics, airline characteristics and route characteristics. It shows that frequency and aircraft size increase with population, income, and runway length. An increase in the proportion of managerial workers in the labor force or the proportion of population below the age of 25 results in greater frequency with the use of small planes. Slot constrained airports and an increase in the number of nearby airports lead to lower flight frequency with the use of smaller planes. Hubs and low cost carriers are associated with larger plane sizes and higher frequency, while regional airline ownership leads to higher frequency and the use of smaller planes. An increase in distance between the endpoints leads to lower frequency with the use of larger planes. As airport delay rises, airlines reduce frequency and use smaller planes, though when airport cancellations rise, flight frequency increases with the use of larger planes.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2009.08.001},
  ISSN                     = {0969-6997},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699709000775}
}

@Article{pathomsiri_impact_2008,
  Title                    = {Impact of undesirable outputs on the productivity of \{{US}\} airports},
  Author                   = {Pathomsiri, Somchai and Haghani, Ali and Dresner, Martin and Windle, Robert J.},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2008},
  Note                     = {00066},
  Number                   = {2},
  Pages                    = {235 -- 259},
  Volume                   = {44},

  Abstract                 = {The paper assesses productivity of 56 \{US\} airports during the period 2000â€“2003. Unlike past work, this study models joint production of both desirable and undesirable outputs (i.e., delays) using the non-parametric directional output distance function. Results are compared to those from models that do not include undesirable outputs. The results show that if delayed flights are excluded from the model, many large but congested airports are found to be efficient. After accounting for delays, small, less congested airports are also found on the efficient frontier. Finally, the model is also used to analyze the effects of other variables.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2007.07.002},
  ISSN                     = {1366-5545},
  Keywords                 = {airport},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S1366554507000762}
}

@Article{pejovic_tentative_2009,
  Title                    = {A tentative analysis of the impacts of an airport closure},
  Author                   = {Pejovic, Tamara and Noland, Robert B. and Williams, Victoria and Toumi, Ralf},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2009},
  Note                     = {00008},
  Number                   = {5},
  Pages                    = {241 -- 248},
  Volume                   = {15},

  Abstract                 = {This paper identifies the vulnerabilities of operations at Heathrow airport to a short airport closure. The system disruptions assessed are delays, flight rerouting or diversions to alternate airports, and flight cancellations. In addition, the impact on fuel consumption, and hence \{CO2\} emissions, is quantified using the Reorganized Air Traffic Control Mathematical Simulator Plus simulation model and the Advanced Emission Model tool. The results have implications for assessing how increased severe weather events associated with climate change may disrupt airport operations.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2009.02.004},
  ISSN                     = {0969-6997},
  Keywords                 = {Airport closure},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699709000143}
}

@TechReport{postgresql_global_development_group_postgresql_2015,
  Title                    = {{PostgreSQL}},
  Author                   = {{PostgreSQL Global Development Group}},
  Institution              = {http://www.postgresql.org/},
  Year                     = {2015},
  Note                     = {00000},

  Urldate                  = {2014-08-20}
}

@Article{pyrgiotis_modelling_2013,
  Title                    = {Modelling delay propagation within an airport network},
  Author                   = {Pyrgiotis, Nikolas and Malone, Kerry M. and Odoni, Amedeo},
  Journal                  = {Transportation Research Part C: Emerging Technologies},
  Year                     = {2013},
  Note                     = {00043},
  Number                   = {0},
  Pages                    = {60 -- 75},
  Volume                   = {27},

  Abstract                 = {We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and captures the â€œripple effectâ€? that leads to the propagation of these delays. The model operates by iterating between its two main components: a queuing engine (QE) that computes delays at individual airports and a delay propagation algorithm (DPA) that updates flight schedules and demand rates at all the airports in the model in response to the local delays computed by the QE. The \{QE\} is a stochastic and dynamic queuing model that treats each airport in the network as a M(t)/Ek(t)/1 queuing system. The \{AND\} model is very fast computationally, thus making possible the exploration at a macroscopic level of the impacts of a large number of scenarios and policy alternatives on system-wide delays. It has been applied to a network consisting of the 34 busiest airports in the continental United States and provides insights into the interactions through which delays propagate through the network and the often-counterintuitive consequences. Delay propagation tends to â€œsmoothenâ€? daily airport demand profiles and push more demands into late evening hours. Such phenomena are especially evident at hub airports, where some flights may benefit considerably (by experiencing reduced delays) from the changes that occur in the scheduled demand profile as a result of delays and delay propagation.},
  Doi                      = {http://dx.doi.org/10.1016/j.trc.2011.05.017},
  ISSN                     = {0968-090X},
  Keywords                 = {Airport delays},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0968090X11000878}
}

@Book{r_core_team_r:_2014,
  Title                    = {R: {A} {Language} and {Environment} for {Statistical} {Computing}},
  Author                   = {{R Core Team}},
  Publisher                = {R Foundation for Statistical Computing},
  Year                     = {2014},

  Address                  = {Vienna, Austria},
  Note                     = {01224},

  Url                      = {http://www.R-project.org/}
}

@Article{rebollo_characterization_2014,
  Title                    = {Characterization and prediction of air traffic delays},
  Author                   = {Rebollo, Juan Jose and Balakrishnan, Hamsa},
  Journal                  = {Transportation Research Part C: Emerging Technologies},
  Year                     = {2014},
  Note                     = {00002},
  Number                   = {0},
  Pages                    = {231 -- 241},
  Volume                   = {44},

  Abstract                 = {Abstract This paper presents a new class of models for predicting air traffic delays. The proposed models consider both temporal and spatial (that is, network) delay states as explanatory variables, and use Random Forest algorithms to predict departure delays 2‚Äì24\&\#xa0;h in the future. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and links (origin‚Äìdestination pairs) in the network, new network delay variables that characterize the global delay state of the entire National Airspace System at the time of prediction are proposed. The paper analyzes the performance of the proposed prediction models in both classifying delays as above or below a certain threshold, as well as predicting delay values. The models are trained and validated on operational data from 2007 and 2008, and are evaluated using the 100 most-delayed links in the system. The results show that for a 2-h forecast horizon, the average test error over these 100 links is 19\% when classifying delays as above or below 60\&\#xa0;min. Similarly, the average over these 100 links of the median test error is found to be 21\&\#xa0;min when predicting departure delays for a 2-h forecast horizon. The effects of changes in the classification threshold and forecast horizon on prediction performance are studied.},
  Doi                      = {http://dx.doi.org/10.1016/j.trc.2014.04.007},
  ISSN                     = {0968-090X},
  Keywords                 = {Air traffic delay prediction},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0968090X14001041}
}

@Article{reynolds-feighan_assessment_1999,
  Title                    = {An assessment of the capacity and congestion levels at {European} airports},
  Author                   = {Reynolds-Feighan, Aisling J. and Button, Kenneth J.},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {1999},
  Note                     = {00077},
  Number                   = {3},
  Pages                    = {113 -- 134},
  Volume                   = {5},

  Abstract                 = {This paper examines the current capacity of the EU's airport infrastructure and the main factors determining that capacity. The nature and role of airport services are detailed. The determination of capacity is examined with discussion of the influence which air traffic control factors, demand characteristics, environmental conditions and engineering design will have on capacity. The methods used to assess delay are detailed along with extensive data sketching the current state of Europe's system of large airports and the extent of infrastructure congestion. The options available to policy makers to improve the management and organisation of capacity are set out and critically discussed.},
  Doi                      = {http://dx.doi.org/10.1016/S0969-6997(99)00006-X},
  ISSN                     = {0969-6997},
  Keywords                 = {Airport infrastructure},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S096969979900006X}
}

@Article{ryerson_time_2014,
  Title                    = {Time to burn: {Flight} delay, terminal efficiency, and fuel consumption in the {National} {Airspace} {System}},
  Author                   = {Ryerson, Megan S. and Hansen, Mark and Bonn, James},
  Journal                  = {Transportation Research Part A: Policy and Practice},
  Year                     = {2014},
  Note                     = {00002},
  Number                   = {0},
  Pages                    = {286 -- 298},
  Volume                   = {69},

  Abstract                 = {Abstract Improved Air Traffic Management (ATM) leading to reduced en route and gate delay, greater predictability in flight planning, and reduced terminal inefficiencies has a role to play in reducing aviation fuel consumption. Air navigation service providers are working to quantify this role to help prioritize and justify \{ATM\} modernization efforts. In the following study we analyze actual flight-level fuel consumption data reported by a major U.S. based airline to study the possible fuel savings from \{ATM\} improvements that allow flights to better adhere to their planned trajectories both en route and in the terminal area. To do so we isolate the contribution of airborne delay, departure delay, excess planned flight time, and terminal area inefficiencies on fuel consumption using econometric techniques. The model results indicate that, for two commonly operated aircraft types, the system-wide averages of flight fuel consumption attributed to \{ATM\} delay and terminal inefficiencies are 1.0‚Äì1.5\% and 1.5‚Äì4.5\%, respectively. We quantify the fuel impact of predicted delay to be 10‚Äì20\% that of unanticipated delay, reinforcing the role of flight plan predictability in reducing fuel consumption. We rank terminal areas by quantifying a Terminal Inefficiency metric based on the variation in terminal area fuel consumed across flights. Our results help prioritize \{ATM\} modernization investments by quantifying the trade-offs in planned and unplanned delays and identifying terminal areas with high potential for improvement.},
  Doi                      = {http://dx.doi.org/10.1016/j.tra.2014.08.024},
  ISSN                     = {0965-8564},
  Keywords                 = {Aviation},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0965856414002043}
}

@InProceedings{schaefer_flight_2001,
  Title                    = {Flight delay propagation analysis with the {Detailed} {Policy} {Assessment} {Tool}},
  Author                   = {Schaefer, L. and Millner, D.},
  Booktitle                = {Systems, {Man}, and {Cybernetics}, 2001 {IEEE} {International} {Conference} on},
  Year                     = {2001},
  Note                     = {00029},
  Pages                    = {1299--1303 vol.2},
  Volume                   = {2},

  Abstract                 = {The Detailed Policy Assessment Tool (DPAT) models the propagation of delay throughout a system of airports and sectors. We present a DPAT analysis to show the effects of simulated changes in capacity due to inclement weather. Local delays are dependent on the capacity to demand ratio of departures and arrivals. We show that propagated effects are significant for the 1st leg after leaving an airport affected by reduced capacities and diminish from leg to leg},
  Doi                      = {10.1109/ICSMC.2001.973100},
  Keywords                 = {airports, air traffic control, air traffic simulation, Analytical models, Delay effects, Delay estimation, delay propagation, delays, Delay systems, detailed policy assessment tool, digital simulation, DPAT analysis, flight delay propagation analysis, inclement weather, Joining processes, Leg, local delays, propagated effects, Propagation delay, simulated changes, Traffic control, travel industry}
}

@Book{silberschatz_database_2010,
  Title                    = {Database {System} {Concepts}},
  Author                   = {Silberschatz, Abraham and Korth, Henry and Sudarshan, S.},
  Publisher                = {McGraw-Hill Science/Engineering/Math},
  Year                     = {2010},

  Address                  = {New York},
  Edition                  = {6 edition},
  Month                    = jan,
  Note                     = {03259},

  __markedentry            = {[Alice:6]},
  Abstract                 = {Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 6th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.}
}

@Article{simic_airport_2015,
  Title                    = {Airport traffic complexity and environment efficiency metrics for evaluation of \{{ATM}\} measures},
  Author                   = {Simić, Tatjana Krstić and Babić, Obrad},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2015},
  Note                     = {00000},
  Number                   = {0},
  Pages                    = {260 -- 271},
  Volume                   = {42},

  Abstract                 = {Abstract For a given (current or planned) traffic demand, different air traffic management measures could result in different airport traffic complexity and efficiency. This paper presents the research on the relationship between airport traffic complexity and time and environmental efficiency for different air traffic control (ATC) tactics applied to the given or planned airport layout. Emphasis is placed on the evaluation of airport traffic complexity, aircraft fuel consumption, gas emissions and time efficiency for different \{ATC\} tactics and/or airport airfield layouts. For busy airports during peak hours, arrival queuing delays, taxi-in, taxi-out times and departure queuing delays increase, which induces additional unnecessary fuel consumption, gas emission and time inefficiency. In order to find a tool which could indicate potential delay generators, a measure of airport traffic complexity â€“ called Dynamic Complexity is proposed. Experiments were performed for airports with different airfield layouts, for different traffic demands and \{ATC\} applied tactics using \{SIMMOD\} simulation model. Traffic situations were analyzed and delays were measured. The values of airport traffic complexity, fuel consumption and gas emissions were also determined. A comparative analysis of the results show: first, the proposed airport traffic complexity metric quite satisfactorily reflects the influence of traffic characteristics upon the environmental state of the system, and second, different \{ATM\} strategic and tactical measures (airport airfield infrastructure development and applied \{ATC\} tactics) could significantly reduce traffic complexity and increase time and environmental efficiency at the airport.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2014.11.008},
  ISSN                     = {0969-6997},
  Keywords                 = {Air traffic management},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699714001471}
}

@Article{soomer_scheduling_2008,
  Title                    = {Scheduling aircraft landings using airlines' preferences},
  Author                   = {Soomer, M. J. and Franx, G. J.},
  Journal                  = {European Journal of Operational Research},
  Year                     = {2008},
  Note                     = {00078},
  Number                   = {1},
  Pages                    = {277 -- 291},
  Volume                   = {190},

  Abstract                 = {Arrival runways are a critical resource in the air traffic system. Arrival delays have a great impact on airline operations and cost. Therefore, tactical arrival planning is becoming increasingly important. In this paper, we consider the tactical single runway arrival problem. The current focus on collaborative decision making is reflected by giving airlines the possibility to provide cost functions related to arrival delays for their flights. A scaling method for these cost is introduced to ensure equity. Our formulation will assign landing times to the flights, while taking the costs into account. A problem-specific local search heuristic has been implemented to obtain reasonable solutions within acceptable computation times. A large number of instances, created using schedule data from a major European hub, have been tested. These experiments show large cost savings for the airlines compared to current practice. All airlines achieve improvements, which is important for the acceptance of the method. The heuristic is able to solve instances with over 100 flights in a few minutes. A simulation experiment shows the method can also be used in a dynamic setting and produces robust schedules.},
  Doi                      = {http://dx.doi.org/10.1016/j.ejor.2007.06.017},
  ISSN                     = {0377-2217},
  Keywords                 = {Airport arrival scheduling},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0377221707005693}
}

@techreport{storm_prediction_center_beaufort_2015,
	title = {Beaufort {Wind} {Scale}},
	institution = {http://www.spc.noaa.gov/faq/tornado/beaufort.html},
	author = {{Storm Prediction Center}},
	year = {2015}
}

@techreport{the_unites_states_department_of_transportation_aviation_1999,
	title = {Aviation {Weather} {Formats}: {METAR}/{TAF}},
	institution = {http://www.uscg.mil/auxiliary/missions/auxair/metar\_taf.pdf},
	author = {{The Unites States Department of Transportation}},
	year = {1999}
}


@TechReport{the_unites_states_department_of_transportation_air_2014,
  Title                    = {Air {Travel} {Consumer} {Report} 2013},
  Author                   = {{The Unites States Department of Transportation}},
  Institution              = {http://www.dot.gov/airconsumer/air-travel-consumer-reports},
  Year                     = {2014},
  Note                     = {00000}
}

@techreport{the_unites_states_department_of_transportation_air_2015,
	title = {Air {Travel} {Consumer} {Report} 2014},
	institution = {http://www.dot.gov/airconsumer/air-travel-consumer-reports},
	author = {{The Unites States Department of Transportation}},
	year = {2015},
	note = {}
}

@article{tiao_asymptotic_1972,
	title = {Asymptotic behaviour of temporal aggregates of time series},
	volume = {59},
	issn = {0006-3444, 1464-3510},
	url = {http://biomet.oxfordjournals.org/content/59/3/525},
	doi = {10.1093/biomet/59.3.525},
	language = {en},
	number = {3},
	urldate = {2015-08-30},
	journal = {Biometrika},
	author = {Tiao, G. C.},
	month = dec,
	year = {1972},
	pages = {525--531}
}


@Article{tu_estimating_2008,
  Title                    = {Estimating flight departure delay distributions—a statistical approach with long-term trend and short-term pattern},
  Author                   = {Tu, Yufeng and Ball, Michael O and Jank, Wolfgang S},
  Journal                  = {Journal of the American Statistical Association},
  Year                     = {2008},
  Note                     = {00068},
  Number                   = {481},
  Pages                    = {112--125},
  Volume                   = {103}
}

@TechReport{TWC2015,
  Title                    = {The {Weather} {Company}},
  Author                   = {{TWC}},
  Institution              = {http://www.theweathercompany.com/},
  Year                     = {2015},
  Note                     = {00000},

  __markedentry            = {[Alice:]}
}

@Article{vlachos_drivers_2014,
  Title                    = {Drivers of airline loyalty: {Evidence} from the business travelers in {China}},
  Author                   = {Vlachos, Ilias and Lin, Zhibin},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2014},
  Note                     = {00000},
  Number                   = {0},
  Pages                    = {1 -- 17},
  Volume                   = {71},

  Abstract                 = {Abstract This paper examines the key factors that determine business traveler loyalty toward full-service airlines in China. Based on literature review and panel interview, ten airline attributes under three categories were derived: (a) operational factors: safety, punctuality, and aircraft; (b) competitive factors: frequency of flights, schedule, frequent flyer program, ticket price, and reputation; and (c) attractive factors: in flight food \&amp; drinks and in flight staff service. We surveyed 2000 Chinese business travelers on domestic flights, obtaining 462 usable questionnaires. Hierarchical regression analysis reveals that reputation, in-flight service, frequent flyer program, and aircraft have the greatest influence in driving airline loyalty.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2014.07.011},
  ISSN                     = {1366-5545},
  Keywords                 = {Airline loyalty},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S136655451400132X}
}

@InProceedings{wang_flight_2003,
  Title                    = {Flight connections and their impacts on delay propagation},
  Author                   = {Wang, P.T.R. and Schaefer, L.A. and Wojcik, L.A.},
  Booktitle                = {Digital {Avionics} {Systems} {Conference}, 2003. {DASC} '03. {The} 22nd},
  Year                     = {2003},
  Month                    = oct,
  Note                     = {00039},
  Pages                    = {5.B.4--5.1--9 vol.1},
  Volume                   = {1},

  Abstract                 = {The authors present a simple analytic model that explicitly separates the controllable factors that influence delays and propagation of delays in the National Airspace System (NAS) from those factors that are random variables in a given scenario. In this paper, the controllable type of factor is called "fixed" and the random type of factor is called "variable." Simple relationship exists among the fixed and variable factors that characterize NAS delay propagation. We show how the model can be applied to better understand delay propagation from specific NAS airports, especially the effects of flight schedule parameters on measured delay. Recorded data from actual NAS operations are used to derive estimates on key model parameters and to show how delay characteristics vary among different airports.},
  Doi                      = {10.1109/DASC.2003.1245858},
  Keywords                 = {airports, delay metrics, delays, delays propagation, flight schedule, NAS airports, National Airspace System, schedule parameters, travel industry}
}

@InProceedings{wieland_limits_1997,
  Title                    = {Limits to growth: results from the detailed policy assessment tool [air traffic congestion]},
  Author                   = {Wieland, F.},
  Booktitle                = {Digital {Avionics} {Systems} {Conference}, 1997. 16th {DASC}., {AIAA}/{IEEE}},
  Year                     = {1997},
  Month                    = oct,
  Note                     = {00016},
  Pages                    = {9.2--1--9.2--8 vol.2},
  Volume                   = {2},

  Abstract                 = {Congestion-induced delay is one of the recognized metrics for assessing performance in the National Airspace System (NAS). Delay can arise from many causes, including factors specific to airline operations as well as factors that can be traced to NAS operations. In this paper we introduce a recently developed tool, the Detailed Policy Assessment Tool (DPAT), and use it to explore delay and the predictability of delay using some simple scenarios for NAS growth},
  Doi                      = {10.1109/DASC.1997.637296},
  Keywords                 = {aerospace computing, Aerospace simulation, airports, air traffic congestion, air traffic control, Air transportation, Business, congestion-induced delay, Costs, data sources, Delay, Delay effects, Delay estimation, delays, detailed policy assessment tool, digital simulation, FAA, National Airspace System, performance, policy assessment tool, predictability, prediction theory, queueing theory, Research and development, scheduling, State estimation, System performance}
}

@Article{wong_survival_2012,
  Title                    = {A survival model for flight delay propagation},
  Author                   = {Wong, Jinn-Tsai and Tsai, Shy-Chang},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2012},
  Note                     = {00013},
  Number                   = {0},
  Pages                    = {5 -- 11},
  Volume                   = {23},

  Abstract                 = {This paper examines flight delay propagation involving a Taiwanese domestic airline. The Cox proportional hazards model is used to develop departure and arrival delay models that show how flight delay propagation can be formulated through repeated chain effects in aircraft rotations. The hazard ratios obtained provide measures of the chances of recovering from flight delays under a variety of situations and the effects that individual contributing factors of flight delays have on airline schedule reliability.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2012.01.016},
  ISSN                     = {0969-6997},
  Keywords                 = {flight delay propagation},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699712000178}
}

@Article{wu_inherent_2005,
  Title                    = {Inherent delays and operational reliability of airline schedules},
  Author                   = {Wu, Cheng-Lung},
  Journal                  = {Journal of Air Transport Management},
  Year                     = {2005},
  Note                     = {00049},
  Number                   = {4},
  Pages                    = {273 -- 282},
  Volume                   = {11},

  Abstract                 = {This paper explores the inherent delays of airline schedules resulting from limited buffer times and stochastic disruptions in airline operations. The reliability of airline schedules is discussed and a set of measuring indices is developed to evaluate schedule reliability. It is found that significant gaps exist between the real operating delays, the inherent delays (from simulation) and the zero-delay scenario. Delay propagation and its impact on schedule reliability are also discussed. Results show that airline schedules must consider the stochasticity in daily operations. Schedules may become robust and reliable, only if buffer times are embedded and designed properly in airline schedules.},
  Doi                      = {http://dx.doi.org/10.1016/j.jairtraman.2005.01.005},
  ISSN                     = {0969-6997},
  Keywords                 = {Schedule reliability},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0969699705000141}
}

@InProceedings{xin_extracting_2006,
  Title                    = {Extracting redundancy-aware top-k patterns},
  Author                   = {Xin, Dong and Cheng, Hong and Yan, Xifeng and Han, Jiawei},
  Booktitle                = {Proceedings of the 12th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
  Year                     = {2006},
  Note                     = {00092},
  Pages                    = {444--453},
  Publisher                = {ACM}
}

@Article{xiong_modelling_2013,
  Title                    = {Modelling airline flight cancellation decisions},
  Author                   = {Xiong, Jing and Hansen, Mark},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2013},
  Note                     = {00001},
  Number                   = {0},
  Pages                    = {64 -- 80},
  Volume                   = {56},

  Abstract                 = {Abstract In order to predict airline responses to Traffic Management Initiatives (TMIs), and reveal the underlying preference structures that shape these responses, we study \{US\} domestic airlines‚Äô cancellation decisions in response to the Federal Aviation Administration (FAA)‚Äôs TMIs, in particular, to Ground Delay Programs (GDPs). By observing the actual flight-cancellation choices made by airline dispatchers, the airlines‚Äô cancellation utility functions can be inferred through the use of binary choice models. The model captures how delays to a given flight and potential delay savings to other flights affect flight cancellation decisions. We also find larger, fuller, less frequent, shorter-distance, and spoke-bound flights are less likely to be cancelled, and that there is inter-airline variation in flight cancellation behaviour.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2013.05.003},
  ISSN                     = {1366-5545},
  Keywords                 = {Airline behavioural modelling},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S1366554513001129}
}

@InProceedings{xu_estimation_2005,
  Title                    = {Estimation of delay propagation in the national aviation system using {Bayesian} networks},
  Author                   = {Xu, Ning and Donohue, George and Laskey, Kathryn Blackmond and Chen, Chun-Hung},
  Booktitle                = {6th {USA}/{Europe} {Air} {Traffic} {Management} {Research} and {Development} {Seminar}},
  Year                     = {2005},
  Note                     = {00029},
  Publisher                = {Citeseer}
}

@InProceedings{zaki_new_1997,
  Title                    = {New {Algorithms} for {Fast} {Discovery} of {Association} {Rules}.},
  Author                   = {Zaki, Mohammed Javeed and Parthasarathy, Srinivasan and Ogihara, Mitsunori and Li, Wei and {others}},
  Booktitle                = {{KDD}},
  Year                     = {1997},
  Note                     = {01176},
  Pages                    = {283--286},
  Volume                   = {97}
}

@Article{zou_flight_2014,
  Title                    = {Flight delay impact on airfare and flight frequency: {A} comprehensive assessment},
  Author                   = {Zou, Bo and Hansen, Mark},
  Journal                  = {Transportation Research Part E: Logistics and Transportation Review},
  Year                     = {2014},
  Note                     = {00004},
  Number                   = {0},
  Pages                    = {54 -- 74},
  Volume                   = {69},

  Abstract                 = {Abstract This paper presents a comprehensive empirical analysis of flight delay impact on airfare and flight frequency in the \{US\} air transportation system. We model airfare and flight frequency as functions of cost and demand characteristics, competition effects, and flight delays at origin, destination, and intermediate hub airports. Estimation results confirm that airlines tend to pass delay cost onto passengers through higher fare, whereas delay has an upward effect on flight frequency. We find that proportionate airport delay reduction across the system can result in annual fare reduction in the order of billion dollars.},
  Doi                      = {http://dx.doi.org/10.1016/j.tre.2014.05.016},
  ISSN                     = {1366-5545},
  Keywords                 = {Delay},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S1366554514000933}
}

@Article{zou_flight_2012,
  Title                    = {Flight delays, capacity investment and social welfare under air transport supply-demand equilibrium},
  Author                   = {Zou, Bo and Hansen, Mark},
  Journal                  = {Transportation Research Part A: Policy and Practice},
  Year                     = {2012},
  Note                     = {00008},
  Number                   = {6},
  Pages                    = {965 -- 980},
  Volume                   = {46},

  Abstract                 = {This paper analyzes benefits from aviation infrastructure investment under competitive supply-demand equilibrium. The analysis recognizes that, in the air transportation system where economies of density is an inherent characteristic, capacity change would trigger a complicated set of adjustment of and interplay among passenger demand, air fare, flight frequency, aircraft size, and flight delays, leading to an equilibrium shift. An analytical model that incorporates these elements is developed. The results from comparative static analysis show that capacity constraint suppresses demand, reduces flight frequency, and increases passenger generalized cost. Our numerical analysis further reveals that, by switching to larger aircraft size, airlines manage to offset part of the delay effect on unit operating cost, and charge passengers lower fare. With higher capacity, airlines tend to raise both fare and frequency while decreasing aircraft size. More demand emerges in the market, with reduced generalized cost for each traveler. The marginal benefit brought by capacity expansion diminishes as the capacity-demand imbalance becomes less severe. Existing passengers in the market receive most of the benefit, followed by airlines. The welfare gains from induced demand are much smaller. The equilibrium approach yields more plausible investment benefit estimates than does the conventional method. In particular, when forecasting future demand the equilibrium approach is capable of preventing the occurrence of excessive high delays.},
  Doi                      = {http://dx.doi.org/10.1016/j.tra.2012.02.015},
  ISSN                     = {0965-8564},
  Keywords                 = {Flight delays},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S096585641200033X}
}

