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Air traffic managers face challenging decisions due to uncertainity in weather and air traffic. One way to support their decisions is to identify similar historical days, the traffic management actions taken on those days, and the resulting outcomes. We develop similarity measures based on quarter-hourly capacity and demand data at four case study airports — EWR, SFO, ORD and JFK. We find that dimensionality reduction is feasible for capacity data, and base similarity on principal components. Dimensionality reduction cannot be efficiently performed on demand data, consequently similarity is based on original data. We find that both capacity and demand data lack natural clusters and propose a continuous similarity measure. Finally, we estimate overall capacity and demand similarities, which are visualized using Metric Multidimensional Scaling plots. We observe that most days with air traffic management activity are similar to certain other days, validating the potential of this approach for decision support.

Scheduled block time (SBT) is the time between gate departure and gate arrival assumed by airlines for use in published timetables and operations planning. SBT setting has critical impacts on airlines’ operating cost and on-time performance. Air carriers regularly update their SBTs to respond to changing operating conditions and evolving business strategies. Most existing studies have focused on investigating the impact of SBT on on-time performance or predicting SBT based on historical performance and market characteristics. However, the dynamics of adjusting SBT, which may allow deeper understanding about the trade-offs airlines make between SBT and on-time performance, have been rarely studied. In this paper, we assume that SBT adjustment choices reveal preferences. Based on airlines’ practice in setting SBT, hypothetical SBT scenarios and their corresponding on-time performance profiles are re-constructed to mimic the situations faced by airline schedulers. This enables us to infer how airlines trade-off between SBT, on-time arrivals, and earliness. By using correlated mixed logit models, we find that our five study airlines are willing to increase SBT from 0.38 to 0.54 min to increase on-time performance by 1%. We also find that both on-time performance and early arrival are valued by airlines, but the former is considerably more valuable. The estimated models can also be used to predict airlines’ SBT adjustments in response to changes in operational performance.

The aviation community is actively investigating initiatives to reduce aircraft fuel consumption from surface operations, as surface management strategies may face fewer implementation barriers compared with en route strategies. One fuel-saving initiative for the air transportation system is the possibility of holding aircraft at the gate, or the spot, until the point at which they can taxi unimpeded to the departure runway. The extent to which gate holding strategies have financial and environmental benefits hinges on the quantity of fuel that is consumed during surface operations. A pilot of an aircraft may execute the taxi procedure on a single engine or utilize different engine thrust rates during taxi because of a delay. In the following study, we use airline fuel consumption data to estimate aircraft taxi fuel consumption rates during the “unimpeded” and “delayed” portions of taxi time. We find that the fuel consumption attributed to a minute of taxi-out delay is less than that attributed to minute of unimpeded taxi time; for some aircraft types, the fuel consumption rate for a minute of taxi delay is half of that for unimpeded taxi. It is therefore not appropriate, even for rough calculations, to apply nominal taxi fuel consumption rates to convert delayed taxi-out time into fuel burn. On average we find that eliminating taxi delay would reduce overall flight fuel consumption by about 1%. When we consider the savings on an airport-by-airport basis, we find that for some airports the potential reduction from reducing taxi delay is as much as 2%.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes are coupled and interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. Collaborative decision-making has shown significant benefits at the airport level, both in the U.S. and in Europe. This paper examines how it could be extended to the multimodal network level, discusses the supporting evidence, and provides recommendations for implementation. A case study on the disruption management following the Asiana Crash at San Francisco International Airport is presented. The crash led to a large number of flight diversions to many airports, such as Oakland, Los Angeles, but also Seattle for instance, disrupting the journeys of thousands of passengers. Passenger reaccommodation varied greatly from airline to airline and airport to airport. First, a passenger-centric reaccommodation scheme is developed to balance costs and delays, for each diversion airport. Second, assuming better information sharing and collaborative decision-making, we show that there was enough capacity at the neighboring airports, Oakland and San Jose, to accommodate most of the diverted flights and reoptimize the allocation of flight diversions to the Bay Area airports. Based on this case study, recommendations for the adoption of multimodal CDM are elaborated. This paper paves the way for further data-driven research for increased resilience of passenger door-to-door journeys.

The aviation community is increasing its attention on the concept of predictability when conducting aviation service quality assessments. Reduced fuel consumption and the related cost is one of the various benefits that could be achieved through improved flight predictability. A lack of predictability may cause airline dispatchers to load more fuel onto aircraft before they depart; the flights would then in turn consume extra fuel just to carry excess fuel loaded. In this study, we employ a large dataset with flight-level fuel loading and consumption information from a major US airline. With these data, we estimate the relationship between the amount of loaded fuel and flight predictability performance using a statistical model. The impact of loaded fuel is translated into fuel consumption and, ultimately, fuel cost and environmental impact for US domestic operations. We find that a one-minute increase in the standard deviation of airborne time leads to a 0.88 min increase in loaded contingency fuel and 1.66 min in loaded contingency and alternate fuel. If there were no unpredictability in the aviation system, captured in our model by eliminating standard deviation in flight time, the reduction in the loaded fuel would between 6.12 and 11.28 min per flight. Given a range of fuel prices, this ultimately would translate into cost savings for US domestic airlines on the order of $120–$452 million per year.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road, or rail, are intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the USA, particularly on the U.S. air transport network and the ground transportation in the Bay Area. The perturbation takes different forms and varies in scale and time frame: cancelations and delays snowball in the airspace, with up to 86% of cancelations in the U.S. due to the SFO crash; highway traffic near the airport is impacted by congestion in previously not congested locations, with low speed and high delays on US 101; and transit passenger demand exhibits unusual traffic peaks in between airports in the Bay Area, with up to 180 passengers more per hour between SFO and Oakland International Airport Bay Area Rapid Transit stations. This paper also investigated the effect of the crash on the social media Twitter. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys and improved transport network resilience.

This paper examines aircraft delays when arriving flights have been assigned specific times to land by air traffic control. It shows that the benefit from aircraft meeting their prescribed times of arrival with higher precision is moderated if it is not complemented by accurate predictions of runway occupancy times (ROTs) after touchdown. A single server queueing system is considered, in which the server corresponds to an airport’s runway. Landing aircraft are assigned scheduled times of arrival at the runway, which they meet with some stochastic error. The time for an aircraft to clear the runway is included as a random variable in the model. A recursive queueing model is formulated and expressions are derived for the mean and variance of aircraft delays. The analysis then focuses on the effect of unpunctual arrivals on delays and how that is affected by dispersion in aircraft ROTs. A simplified situation, in which model parameters are the same for all aircraft, shows that dispersion in ROTs causes significant losses in throughput efficiency, substantially reducing the benefit of precise trajectory adherence in many cases.

This paper presents a game theoretic model of a sequential capacity allocation process in a congestible transportation system. In this particular application, we investigate the governing principles at work in how airlines will time their requests for en route resources under capacity shortfalls and uncertain conditions, when flights are not able to take their preferred route at their preferred departure time slot due to the shortfalls. We examine a sequential “First Submitted First Assigned” (FSFA) capacity allocation process within an en route air traffic flow management (ATFM) program such as the Collaborative Trajectory Options Program (CTOP), which is a Federal Aviation Administration initiative that aims to manage en route capacity constraints brought on by inclement weather and capacity/demand imbalances. In the FSFA process, flights are assigned the best available routes and slots available at the time flight operators submit their preference requests during the planning period, in a sequential manner. Because flight operators compete with one another for resources, in such an allocation process they would be expected to make their requests as early as possible. However, because weather and traffic conditions – and therefore, the values of resources – can change significantly, flight operators may prefer to request resources later in the process rather than earlier. We use a game theoretic setup to understand how flight operators might tradeoff these conflicts and choose an optimal time to submit their preferences for their flights, as submission times are competitive responses by flight operators looking to maximize their benefits. We first develop a loss function that captures the expected utility of submitting preferences under uncertainty about operating conditions. Then, a conceptual model of the FSFA process is constructed using a simultaneous incomplete information game, where flight operators compete for the “prizes” of having submitted their inputs before others. A numerical study is performed in which it is demonstrated that preference submission times are heavily influenced by the general uncertainty surrounding weather and operational conditions of the ATFM program, and each flight operator’s internal ability to handle this uncertainty. A key finding is that, in many of the scenarios presented, an optimal strategy for a flight operator is to submit their preferences at the very beginning of the planning period. If air traffic managers could expect to receive more submissions at the beginning of the planning period, they could more easily coordinate the ATFM program with other ATFM programs taking place or scheduled to take place, and they would have more opportunity to call another FSFA allocation route before the ATFM program begins, should conditions change enough to warrant this. Outputs of the model may provide some general insights to flight operators in planning submission strategies within competitive allocation processes such as FSFA. Also, this work may have a broader application to other sequential resource allocation strategies within congestible and controlled transportation systems.

The LAX North Airfield Safety Study was undertaken by an Academic Panel consisting of the present authors, and was based in large part on a simulation that was conducted at FutureFlight Central at NASA Ames Research Center. The primary aim of the study was “to estimate as specifically as possible the level of future safety associated with several geometrical configurations of the LAX North Airfield.” This paper describes the study, and how it combined information from human-in-the-loop simulations at NASA with historical data from LAX and other US airports about runway incursions and collisions. The analysis indicated that, even under its existing physical layout, LAX North would experience very low risk of runway collisions at traffic levels projected for 2020. That risk could be reduced by about half if the North Airfield runways were reconfigured, and some reconfigurations would also add appreciably to the operational efficiency of the airport. But because the “baseline” level of risk is so low, the Study concluded that “it would be difficult to construct a compelling case on safety grounds alone for reconfiguring the North Airfield.”

While in ground transportation the concept of reliability has been extensively studied, there is little literature in air transportation. Scheduled block time (SBT) setting is a crucial part in airlines’ scheduling. Interviews with an airline and relevant work in ground transportation have shown that SBT and the historical block time distribution, reflecting block time reliability, have a close relationship. This paper investigates how the change in actual block time distribution will affect SBT and system performance. Firstly this relationship is studied with empirical data and multiple regression models. The distribution of the historical block time for a flight is depicted by the difference between every 10th percentiles. We found that gate delay plays a minor role in setting SBT and that SBTs have decreasing sensitivity to historical flight times toward the right tail of the distribution. To specifically link SBT setting with the flight’s on-time performance, a SBT adjustment model is further developed. Poor on-time performance leads to increased SBT in the next year. With the behavior model results showing that both the median block time and the “inner right tail” of the distribution affect SBT setting, an impact study is conducted to validate these impacts with historical data. The impact of historical block time distribution on SBT is validated with real data in year 2006–2008 and 2009–2011. Furthermore, by studying the flight performance difference based on different changes in SBT, we conclude that ignoring the impact on SBT changes when considering potential benefits of improved block time distribution could lead to inaccurate results.

In this paper we study the 4D trajectory planning problem in a contrail sensitive environment. We identify the control inputs that steer the aircraft from the initial fix to the final fix following a horizontal route of waypoints while performing step climbs and descents, in order to minimize the overall flying cost of fuel consumption, CO2 emissions, passenger travel time, and persistent contrail formation. The optimal trajectory design problem is formulated as a multiphase mixed integer optimal control problem, which is converted into a mixed integer non-linear program by first making the unknown switching times part of the state, then applying a Hermite-Simpson direct collocation method, and finally introducing binary variables to model the decision making. We solve the mixed-integer nonlinear program using a branch-and-bound algorithm. The numerical results show the effectiveness of the approach. This research was carried out at NEXTOR, University of California at Berkeley, where Manuel Soler was a visiting scholar hosted by Prof. Mark Hansen. The authors would like to thank all members of NEXTOR at UC Berkeley for fruitful and interesting discussions. This work was partially financed by Universidad Rey Juan Carlos (URJC) encouraging mobility in PhD candidates with the following program: Programa propio de fomento y desarrollo de la investigación. This research was also partially sponsored by the NASA Ames-UC Santa Cruz, University Affiliated Research Center Aligned Research Program.

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 1.0–1.5% and 1.5–4.5%, respectively. We quantify the fuel impact of predicted delay to be 10–20% 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.

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 – 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.

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.

Past evaluations of airport surface operations automation technologies have focused on capacity utilization, delay mitigation and fuel efficiency impacts. Predictability, while recognized as an important operational performance goal, has received little attention. One reason could be that applicable predictability metrics have not been developed in the context of airport surface operations management. This research fills the gap by proposing metrics for predictability performance evaluation. Using results from a SARDA human-in-the-loop simulation conducted at NASA Ames’ Future Flight Central, we present a comprehensive assessment of the predictability impacts of airport surface automation. A wide range of the impacts is considered, which includes variability in taxi-out time, predictability of take-off time and take-off sequence, entropy of the airfield state, and perceived predictability from users.

Ground delay programs (GDPs) are frequently used to keep U.S. air transportation safe and efficient. Most research on GDPs has focused on optimal design and implementation, with little attention given to retrospective performance evaluation. This research fills that gap by identifying GDP performance criteria, developing associated performance metrics, and evaluating GDP performance metrics across airports and over time. GDP performance criteria are established, and associated performance metrics are specified for five performance goals: capacity utilization, efficiency, predictability, equity, and flexibility. By defining multiple performance metrics, this research enables FAA traffic managers and flight operators to review GDP performance after the fact in a comprehensive way and to uncover GDP performance trends across airports and over time. Through use of data from the FAA Aggregate Demand List and the FAA Aviation System Performance Metrics, historical GDP performance is assessed for San Francisco I...

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 – 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 Administration’s (FAA’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.

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’ cancellation decisions in response to the Federal Aviation Administration (FAA)’s TMIs, in particular, to Ground Delay Programs (GDPs). By observing the actual flight-cancellation choices made by airline dispatchers, the airlines’ 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.

This paper develops methodologies for generating probabilistic capacity scenarios from two terminal weather forecasts: The Terminal Aerodrome Forecast (TAF) and the San Francisco Marine Initiative (STRATUS) forecast. The scenarios are assessed by using them as inputs to a static stochastic Ground Delay Program (GDP) model to determine efficient Air Traffic Flow Management (ATFM) strategies, and determining the effectiveness of the strategies in reducing the realized cost of delay. We use San Francisco International airport as a case study to quantify the benefit of using weather forecasts in decision making. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 17%-23% compared to scenarios that do not make use of this information. The paper also compares the cost of delays using strategies determined using the scenarios generated from TAF and STRATUS. It is shown that on average TAF-based scenarios result in delay costs of similar magnitudes to STRATUS-based ones. The methodologies developed using the TAF can be applied to other airports to better plan operations during a GDP.

Airspace Flow Programs (AFPs) assign ground delays to flights in order to limit flow through capacity constrained airspace regions. AFPs have been successful in controlling traffic with reasonable delays, but a new program called the Combined Trajectory Options Program, or CTOP, is being explored to further accommodate projected increases in traffic demand. In CTOP, centrally managed rerouting and user preference inputs are also incorporated into initial en route resource allocations. We investigate four alternative versions of resource allocation within CTOP in this research, under differing assumptions about the degree of random variability in airline flight assignment costs when measured against a simple model based upon the flight specific, but otherwise fixed, ratio of airborne flight time and ground delay unit cost. Two en route resource allocation schemes are based on ordered assignments that are similar to those used currently, and the other two are system-optimal assignment schemes. One of these system-optimal schemes is based on complete preference information, which is ideal but not realistic, and the other is based on partial information that may be feasible to implement but yields less efficient assignments. The main contribution of this research is a methodological framework to evaluate and compare these alternative en route resource allocation schemes, under varying assumptions about the information traffic managers have been provided about the flight operators’ route preferences. The framework allows us to evaluate these various schemes under differing assumptions about how well the traffic managers’ flight cost model captures flight costs. A numerical example demonstrates that a sequential resource allocation scheme – where flights are assigned resources in the order in which preference information is submitted – can be more efficient than a scheme that offers a cost minimizing allocation based on less complete preference information, and may at the same time be perceived as equitable. We also find that assigning resources in the order flights are scheduled results in less efficient allocations, but more equitable ones.

Air traffic management (ATM) initiatives are developed and implemented to mitigate delays caused by uncertainty in weather and demand at an airport. ATM decisions are made by traffic flow management specialists, based on their judgment and experience. Historical data on airport operations can be used to assist decision-making by intelligently augmenting controller experience with a more systematic and complete record of past ATM actions under similar conditions and their consequences. A decision-support tool that finds days similar to a query day with regard to weather features driving capacity can be used to guide day-of-operations decisions and assess past performance. A framework to evaluate different similarity measures is developed based on operational outcomes of the airport.

Air carriers and aircraft manufacturers are investing in technologies and strategies to reduce fuel consumption and associated emissions. This chapter reviews related issues to assess airline fuel efficiency and offers various empirical evidences from our recent work that focuses on the U.S. domestic passenger air transportation system. We begin with a general presentation of four methods (ratio-based, deterministic frontier, stochastic frontier, and data envelopment analysis) and three perspectives for assessing airline fuel efficiencies, the latter covering consideration of only mainline carrier operations, mainline–subsidiary relations, and airline routing circuity. Airline fuel efficiency results in the short run, in particular the correlations of the results from using different methods and considering different perspectives, are discussed. For the long-term efficiency, we present the development of a stochastic frontier model to investigate individual airline fuel efficiency and system overall evolution between 1990 and 2012. Insight about the association of fuel efficiency with market entry, exit, and airline mergers is also obtained.

This paper analyzes the welfare effects of slot controls on major US airports. We consider the fundamental tradeoff between benefits from queuing delay reduction and costs due to simultaneous schedule delay increase to passengers while imposing slot limits at airports. A set of quantitative models and simulation procedures are developed to explore the possible airline scheduling responses through reallocating and trimming flights. We find that, of the 35 major US airports, a more widespread use of slot controls would improve travelers’ welfare. The results from our analyses suggest that slot caps at the four airports that currently have slot controls (Washington Reagan, Newark, New York LaGuardia, New York John F. Kennedy) are set too high. Further slot reduction by removing some of the flights at these airports could generate additional benefits to passengers. Slot controls can potentially reduce two thirds of the total system delays caused by congestion. A number of implementation and design issues related to the use of slot controls are also discussed in the paper.

Adverse weather is the dominant cause of delays in the National Airspace System (NAS). Since the future weather condition is only predictable with a certain degree of accuracy, managing traffic in the weather-affected airspace is a challenging task. In this paper, the authors propose a geometric model to generate an optimal combination of ground delay and route choice to hedge against weather risk. The geometric recourse model (GRM) is a strategic Probabilistic Air Traffic Management (PATM) model that generates optimal route choice, incorporating route hedging and en-route recourse to respond to weather change: hedged routes are routes other than the nominal or the detour one, and recourse occurs when the weather restricted airspace becomes flyable and aircraft are re-routed to fly direct to the destination. Among several variations of the GRM, we focus on the hybrid Dual Recourse Model (DRM), which allows ground delay as well as route hedging and recourses, when the weather clearance time follows a uniform distribution. The formulation of the hybrid DRM involves two decision variables -- ground delay and route choice -- and four parameters: storm location, storm size, maximum storm duration time, and ground-airborne cost ratio. The objective function has two components: expected total ground delay cost and expected total airborne cost. The authors propose a solution algorithm that guarantees to find the global optimum of the hybrid-DRM. Based on the numerical analysis, the authors find that ground-holding is effective only when combined with the nominal route. Otherwise, it is optimal to fly on the route determined by the DRM without ground delay. The authors also find the formula of the threshold ground-airborne cost ratio, which we call the Critical Cost Ratio (CCR), that determines the efficacy of ground delay: the higher the CCR, the more effective the strategies involving ground delay. The authors conclude that both ground delay and route hedging should be considered together to produce the best ATM decisions.

To assist in aviation systems planning in the context of fuel price uncertainty and environmental regulation, we take a total logistics cost approach and evaluate three representative aircraft (narrow body, regional jet, and turboprop) for operating and passenger preference costs over a range of fuel prices. Homogenous fleets of each vehicle category are compared for operating and passenger costs over a range of fuel prices and route distances and the minimum cost fleet mix is determined. In general, as fuel prices increase, the turboprop offers a lower operating cost per seat over a wider range of distances when compared with both jet aircraft models. The inclusion of passenger costs along with operating costs decreases the number of fuel price and distance pairs where the turboprop exhibits the lower cost. This analysis shows that the aircraft that exhibits the lowest cost is highly sensitive to fuel prices and passenger costs and points to the important balance between saving fuel and serving passengers.

In this paper, we address the problem of making inferences about a population of infrastructure facilities from a subset that is a biased sample. We consider the case in which the sample is biased toward facilities in worse condition or requiring more expensive repair. Two methods are developed that incorporate a model of the process through which the sample is selected. One of the methods is based on well-known truncated distributions, whereas the other assumes that the bias operates continuously. The methods are applied to a class of facilities under the Federal Aviation Administration’s jurisdiction known as “unstaffed facilities.” These consist of structures housing radars, navigation aids, radio beacons, and other ground-based equipment, and no previous system-wide evaluation has been attempted for these facilities. We present and discuss the estimates obtained from both the methods, and examine their goodness-of-fit with the sample. Given the premise that bias exists, the continuous bias model prove...

Flight cancellations are costly to airlines and air passengers, but the question is, how costly? Little quantitative analysis about cancellation cost has been done, and current studies are focused on flight delays. However, cancellations should be studied to supplement delay metrics to assess the performance of the National Airspace System, as well as to evaluate the effectiveness of air traffic management. Postoperation data of the ground delay program were explored, and relative value of cancellations in terms of delay savings were inferred. Two cancellation metrics were developed for this purpose. In addition, flight cancellation decisions were modeled with a discrete choice model by using the two cancellation metrics that were generated as well as some other flight characteristics. Three models are presented in this study. The results reveal the value of a flight cancellation and airline preference structure in making decisions in canceling a flight in response to schedule disturbance. The study contributes to the literature in a distinct way: post-operation data are used to understand airline flight cancellation behavior in the context of ground delay programs.

In this paper, we present a stochastic integer programming model for managing air traffic inbound to an airport when both the airport itself and its approach routes are subject to adverse weather. In the model, ground delay decisions are static, while those on rerouting are dynamic. The decision variables in the model are aggregate number of flights planned to arrive at various capacity constrained resources. The model does not directly assign arrival times to individual flights. Therefore, in context of Collaborative Decision Making, which is the governing philosophy of the air traffic management system of the United States, the solutions from the dynamic rerouting model can be directly fed to some resource allocation algorithm that assigns routes and release times to individual flights or to the airlines who operate them. When adverse weather blocks or severely limits capacity of terminal approach routes, rerouting flights onto other approaches yields substantial benefits by alleviating high ground delays. Our experimental results indicate that making rerouting decisions dynamically results in 10–15% delay cost reduction compared to static rerouting, and about 50% less delay cost compared to a “pure” ground holding strategy (i.e., no rerouting). In contrast to static rerouting, the dynamic rerouting capability results in making rerouting decisions that are better matched to realized weather conditions. Lower total expected delay cost is achieved by delaying the rerouting decisions for flights until they reach the divergence point between alternative routes, and hence exploiting updated information on weather while making those decisions. In cases where the airport is the main, but not the only, bottleneck, the dynamic rerouting model may assign higher ground delays so that the rerouting decisions can be deferred until more information on en route weather becomes available.

There has been a unique convergence of factors that have contributed to Japan's outstanding public transportation system. The paper focuses on two modes of transportation: air and high-speed rail (HSR). These two modes do not complement each other as in Europe, but compete head-on in Japan. Moreover, Japan does not have to contend with market-distorting public subsidies, because both companies operating HSR in the corridor of interest are highly profitable, primarily because of their HSR service. The Japanese transportation system therefore provides an excellent environment in which to study the nature of competition between air and HSR. (Conventional rail is also discussed where appropriate.) Individual contributors to terminal pair choice are contrasted between air and HSR, and the trade-offs between accessibility, frequency, and speed are analyzed in detail.

A strategy for airline recovery from schedule perturbation caused by adverse weather or other temporary events is proposed. Capacity reduction at a hub airport caused by these temporary events is reflected as reduced slots (or later controlled time of arrivals) for airlines from ground delay programs. It is suggested that under severe circumstances legacy airlines with hub-and-spoke networks implement ground transportation into their network to substitute for flights. This strategy is called real-time intermodal substitution (RTIMS). RTIMS is different from air-rail cooperation practiced in Europe in that it is triggered only by a severe demand and supply imbalance at major hub airports and it performs operational integration of airside and ground transportation. Mathematical programming is presented to help airlines make decisions about if and how to delay, canceling flights, or substituting flights with buses. An approximation algorithm is proposed to obtain solutions, avoiding substantial computation time required to solve large-scale nonlinear integer programming. As an evaluation, experimental data are used to compare scenarios with and without tactical intermodal substitution.

Airlines and Air Navigation Service Providers are united in their goal to reduce fuel consumption. While changes to flight operations and technology investments are the focus of a number of studies, our study is among the first to investigate an untapped source of aviation fuel consumption: excess contingency fuel loading. Given the downside risk of fuel exhaustion of diverting to an alternate airport, airline dispatchers may load excess fuel onto an aircraft. Such conservatism comes at a cost of consuming excess fuel, as fuel consumed is a function of, among other factors, aircraft weight. The aim of this paper is to quantify, on a per-flight basis, the fuel burned due to carrying fuel beyond what is needed for foreseeable contingencies, and thereby motivate research, federal guidance, and investments that allow airline dispatchers to reduce fuel uplift while maintaining near zero risks of fuel exhaustion. We merge large publicly available aviation and weather databases with a detailed dataset from a major US airline. Upon estimating factors that capture the quantity fuel consumed due to carrying a pound of weight for a range of aircraft types, we calculate the cost and greenhouse gas emissions from carrying unused fuel on arrival and additional contingency fuel above a conservative buffer for foreseeable contingencies. We establish that the major US carrier does indeed load fuel conservatively. We find that 4.48% of the fuel consumed by an average flight is due to carrying unused fuel and 1.04% of the fuel consumed by an average flight is due to carrying additional contingency fuel above a reasonable buffer. We find that simple changes in flight dispatching that maintain a statistically minimal risk of fuel exhaustion could result in yearly savings of 338 million lbs of CO2, the equivalent to the fuel consumed from 4760 flights on midsized commercial aircraft. Moreover, policy changes regarding maximum fuel loads or investments that reduce uncertainty or increase the ability to plan flights under uncertainty could yield far greater benefits.

In this paper, we build an aggregate demand model for air passenger traffic in a hub-and-spoke network. This model considers the roles of airline service variables such as service frequency, aircraft size, ticket price, flight distance, and number of spokes in the network. It also takes into account the influence of local passengers and social-economic and demographic conditions in the spoke and hub metropolitan areas. The hub airport capacity, which has a significant impact on service quality in the hub airport and in the whole hub-and-spoke network, is also taken into consideration. Our demand model reveals that airlines can attract more connecting passengers in a hub-and-spoke network by increasing service frequency than by increasing aircraft size in the same percentage. Our research confirms the importance of local service to connecting passengers, and finds that, interestingly, airlines’ services in the first flight leg are more important to attract passengers than those in the second flight segment. Based on data in this study, we also find that a 1% reduction of ticket price will bring about 0.9% more connecting passengers, and a 1% increase of airport acceptance rate can bring about 0.35% more connecting passengers in the network, with all else equal. These findings are helpful for airlines to understand the effects of changing their services, and also useful for us to quantify the benefits of hub airport expansion projects. At the end of this paper, we give an example as an application to demonstrate how the developed demand model could be used to valuate passengers’ direct benefit from airport capacity expansion.

This paper explores the impact of unscheduled navigational and surveillance equipment outages on airport performance during visual flight rules and instrument flight rules at San Francisco International Airport and Phoenix Sky Harbor International Airport. The following most common unscheduled short outages, which occurred during January 2000-December 2002, are analyzed in detail: very high frequency omnidirectional range (VOR), approach lighting system type 2 (ALSF-2), the secondary radar Mode S, and air traffic control radio beacon system (ATCRBS). We explore the air traffic control procedures and responses to unscheduled outages, and develop a methodology to examine the effects of unscheduled outages on airport performance. Two Federal Aviation Administration databases, maintenance management system and aviation system performance metrics, are used to construct censored regression models (i.e., Tobit models) to assess the airport throughput performance during such unscheduled outages. It is found that airport arrival and departure throughputs do not deteriorate with VOR and ALSF-2 outages due to availability of air traffic procedures that allow alternative approaches into the airport. However, the unscheduled outages of secondary radar Mode S and ATCRBS do cause airport throughput degradations.

New statistical methods for assessing airport capacity and delay, especially during rare events and after infrastructure upgrades, are developed and demonstrated in this article. Models of airport throughput are estimated with the new methods and the developed models are checked against the opening of a new runway in Detroit. Simulated delays match actual delays effectively, though the articles states limits for the model. The author suggests introducing autocorrelation into censored regression models to compensate for model limits. More generally, the author's study allows the Federal Aviation Administration (FAA) to control for changes in demand and weather that would otherwise confound before-and-after, with/without studies of host factors that affect NAS performance.

Explored are the benefits of integrating User Request Evaluation Tool (URET) and Controller-Pilot Data Link Communications (CPDLC). Controller-pilot voice-communication messages and aircraft traffic flows and conflicts are analyzed in great detail in one representative, URET-operating en route sector. On the basis of the mapped URET data and the real-world communication messages, a base case and two alternative scenarios were analyzed to estimate the number of clearances that are given to pilots to resolve aircraft conflicts a sufficient time before the start of the conflict, and to determine the reduction in frequency congestion possible if such messages were sent via data link. It was found that the highest frequency use, which corresponded to the first traffic peak, was reduced 27% after the second-scenario messages were removed from the base-case scenario. After removing the non-time-critical conflict-resolution messages, the total reduction was 59%. Frequency use during the highest number of aircraft conflicts was reduced 65% after all messages from the second and the third scenario were removed. Thus, the benefits of integrating CPDLC and URET are significant. If non-time-critical conflict messages were transmitted via data link in the integrated CPDLC and URET environment, they could considerably improve the frequency congestion. More important, the largest benefits would be experienced in situations involving a large number of aircraft conflicts, or during busy periods of traffic. These improvements could further help to reduce the number of communication errors (and the consequent air traffic control workload), as well as the number of operational errors.

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 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 “schedule delay” 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.

Free Flight Phase 1 (FFP1) is an FAA program for improving the performance of the National Airspace System (NAS) through the deployment of advanced technologies for air traffic management. In addition to the deployment activities, FFP1 includes a significant evaluation component, which faces a significant hurdle. A plethora of factors—weather, demand, enhancements to the NAS infrastructure not related to FFP1, facility outages, and so on—may also cause changes in NAS performance. It is necessary to normalize for these factors in order to determine the effect of FFP1. Normalization procedures to isolate the impact of the implementation of an FFP1 technology—the Terminal Area Traffic Management Advisor (T-TMA)—are documented at the Southern California TRACON, where it is used for controlling traffic into Los Angeles International Airport. Two examples of normalization are presented. One examines the effect of T-TMA on airport arrival capacity, and the other looks at arrival delay. The results, although preliminary given the short time since implementation, are consistent: it appears that capacities have increased and delays decreased as a result of the deployment of the tool. Moreover, the magnitudes of the delay reductions and capacity increases are consistent.

The relationship between the performance of the US National Airspace System (NAS) and airline costs is examined by estimating airline cost functions that include NAS performance metrics as arguments, using quarterly data for 10 US domestic airlines. Performance metrics that vary by airline and quarter are developed by applying principal component analysis to seven underlying variables, including average delay, delay variance, and the proportion of flights that is cancelled. This analysis reveals that variation in the seven variables can be adequately captured by three or fewer factors, which we term NAS performance factors. If three factors are used, they can be interpretted as “delay”, “variability”, and “disruption”, the latter two of which are merged into a single “irregularity” factor in the two-factor model. Cost function estimation results confirm the anticipated link between NAS performance and airline cost. In the cost models with two and three performance factors, the irregularity and disruption factors are found to have the strongest cost impacts. These results challenge the prevailing assumption that delay reduction is the most important benefit from NAS enhancements. Using the estimated cost models, we predict airline cost savings from substantially improved NAS performance in the range $1–4 billion annually.

This paper describes a model-free method for estimating some yield metrics that are used to track integrated circuit fabrication processes. Our method uses binary probe test data at the wafer level to estimate the size, shape and location of large-area defects or clusters of defective chips. Unlike previous methods in the yield modeling literature, our approach makes extensive use of the location of failing chips to directly identify clusters. An important by product of this analysis is a decomposition of wafer yield that attributes defective chips to either large- or small-area defects. Simulation studies show that our procedure is superior to the time-honored windowing technique for achieving a similar breakdown. In addition, by directly estimating defect clusters, we can provide engineers with a greater understanding of the manufacturing process. It has been our experience that routine identification of the spatial signatures of clustered defects and associated root-cause analysis is a cost-effective approach to yield and process improvement.

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.

This paper develops a queueing model for trajectory-based aircraft operations, a cornerstone of the Next Generation Air Transportation System. Aircraft are assigned scheduled times of arrival at a server, which they meet with some normally distributed stochastic error. A recursive queueing model with deterministic service times is formulated, and Clark's approximation method is employed to estimate each flight’s expected queueing delay. The approximation method is then employed to estimate optimal headways between paired aircraft arrivals at San Francisco International Airport.

It has become cliche to observe that new information technologies endanger privacy. Typically, the threat is viewed as coming from Big Brother (the government) or Company Man (the firm). But for a nascent data practice we call "self-surveillance," the threat may actually come from ourselves. Using various existing and emerging technologies, such as GPS-enabled smartphones, we are beginning to measure ourselves in granular detail - how long we sleep, where we drive, what we breathe, what we eat, how we spend our time. And we are storing these data casually, perhaps promiscuously, somewhere in the "cloud," and giving third-parties broad access. This data practice of self-surveillance will decrease information privacy in troubling ways. To counter this trend, we recommend the creation of the Privacy Data Guardian, a new profession that manages Privacy Data Vaults, which are repositories for self-surveillance data. In Part I, we describe the emerging data practice of self-surveillance, which has been enabled by various new measurement and communication technologies. We explain how self-surveillance can produce substantial benefits to both the individual and society, in both intrinsic and instrumental terms. Unfortunately, such benefits may never be achieved without substantial privacy costs. Part II makes threshold clarifications about those privacy costs. It proffers two different metrics by which privacy might be measured and explains why the rise of self-surveillance will entail the net loss of privacy under either metric. We also point out that the problem of self-surveillance (our surveilling us) is, fortunately, more tractable than related privacy problems, such as third-party surveillance of us and our surveillance of third-parties. Having cleared this brush, we turn to our central proposal - the creation of the Personal Data Guardian, a professional whose job it is to maintain a client’s self-surveillance data in a Personal Data Vault. In addition to providing technical specifications of this approach, we outline the specific legal relations, which include a fiduciary relationship, between client and Guardian. In addition, we recommend the creation of an evidentiary privilege, similar to a trade secret privilege, that protects self-surveillance data held by a licensed Guardian. Finally, Part IV answers objections that our solution is implausible or useless. We conclude by pointing out that various legal, technological, and self-regulatory attempts at safeguarding privacy from new digital, interconnected technologies have not been particularly successful. Before self-surveillance becomes a widespread practice, some new innovation is needed. In our view, that innovation is a new "species," the Personal Data Guardian, created through a fusion of law and technology and released into the current information ecosystem.

*† This paper develops a queueing model for aircraft landings at a single runway under trajectory-based flight operations. The situation is expected to arise in the Next Generation Air Transportation System. Aircraft are assigned scheduled times of arrival, which they meet with some stochastic error. It is assumed that errors are small enough for scheduled order to be maintained, and they follow a normal distribution. A recursive queueing model is formulated, and Clark's approximation for the maximum of a finite set of random variables is employed to analytically approximate the mean and variance of flight delays. For a given airport demand and capacity it is shown that total delay is proportional to the standard deviation of the stochastic error; this result facilitates the estimation of the system's total delay through lookup tables. The approximation method is found to be reasonably accurate and very fast computationally.

The Virtual Airspace Modeling and Simulation (VAMS) project developed by the National Aeronautics and Space Administration (NASA) presents a detailed plan for increasing National Airspace System capacity. Interviews with aviation experts regarding the VAMS project led to lessons learned which can inform current modernization plans and processes, as the current system prepares for modernization. According to experts consulted, development should include a small number of project developers who provide periodic opportunities for wide stakeholder feedback; roadmaps should incorporate uncertainly and provide project guidance on a high level; and simulation tools are valuable to modernization efforts, yet assumptions should be documented and their sensitivity understood.

This paper reports the first application of iterative experimental design methodology for high spatiotemporal resolution characterization of river and lake aquatic systems performed using mobile robot sensing systems. Both applications involve dynamic phenomena spread over large spatial domain: 1) Characterization of contaminant concentration and flow at the confluence of two major rivers displaying dynamics due to flow of the water; and 2) Characterization of rapidly evolving biological processes such as phytoplankton dynamics in a lake system. We describe the development and application of a new general purpose method for mobile robot sensing in such environments - Iterative experiment Design for Environmental Applications (IDEA). IDEA introduces in-field adaptation of mobile robotic sensing system. Analysis of the complex spatial and temporal structures associated with each observed environment is presented. Detailed characterization of the observed environment using IDEA methodology is used as an informed prior to improve the performance of the existing adaptive experimental design approaches for mobile robotic systems - stratified adaptive sampling and hierarchical non-stationary Gaussian Processes.

In this paper, the feasibility of using elevator motion as a statistically repeatable excitation for use in structural health monitoring (SHM) of tall buildings is investigated. Robust SHM benefits from a repeatable excitation source, but in buildings this is problematic for many technical and practical reasons. Ambient vibrations are therefore often used for repeated system identification of such civil structures. In tall buildings, the movement of elevators contributes to the mix of ambient vibrations. The 17-story Factor Building on the UCLA campus is heavily instrumented for earthquakes, and has high-resolution real-time continuous recording of vibration data. There are 72 accelerometers distributed throughout the building in addition to nearby ground motion and borehole reference accelerometers. Using Factor as a test bed, experimental studies of elevator-induced building vibrations were conducted. Building monitoring was augmented with additional elevator sensors and a small shaker. Initial results indicate that elevator-induced vibrations are statistically separable from other ambient vibration components in both the time and frequency domains. However, it may be necessary to measure elevator position and/or elevator counterweight vibrations. Work continues on the development of a methodology to use the elevators as a repeatable source for SHM of the Factor Building and other tall buildings.

This paper presents an overview and case studies of Performance -based System capability of Next Generation Aviation Transportation System (NGATS), specifically the case of Required Navigation Performance. The Required Navi gation Performance (RNP), one of the key capabilities of Performance -based system, defines lateral navigational precision level, which provides the opportunity to reduce fuel, negative environmental effect, airline operation disruptions, and to increase ru nway utilization and safety. In the early U.S. cases, airlines took a leading role in development of RNP, based on the strong economical motivation and clear business objectives. Reduction in diversions, cancellation, and fuel consumption translated int o significant cost saving and better service quality. There are also cases that RNP increased payload and runway utilization rate. Selective decision process to deploy RNP at the locations where most suitable, showed a great potential of Performance Base d Navigation, yet the implementation process was still limited by system provider ’s capability and the authorization process . In this paper, we will present three early RNP cases in US, including Alaska Airlines, Jetblue Airway, Continental Airlines, and discuss what RNP and Performance Based Navigation can truly deliver and how future development should be carried out.

Ecological observatories are a new class of multiuser research infrastructure designed and deployed to address a broad range of continental-scale ecological questions that until only recently were not technologically feasible. These highly networked ecological observatories, spread across the United States and featuring a diverse integration of programmable sensing capabilities and remote observational functions are expected to enable a transformation in the scope of environmental research, particularly in relation to understanding how global climate shifts and local and regional land use changes will quantitatively affect the composition, structure, and dynamics of the nation’s ecosystems and services. Observing systems research focused on terrestrial ecology is one of four core research application fields of the Center for Embedded Networked Sensing, which operates an engineering and experimental test bed located at the James San Jacinto Mountains Reserve, a biological field station that is part of the University of California Natural Reserve System. This paper describes the various experimental and deployed embedded networked sensing systems at the James Reserve, and suggests how similar systems and related infrastructure will be key to meeting the engineering and science objectives of future ecological research.