Data-Driven Modeling of the Airport Configuration Selection Process

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choice modeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial “nested logit” model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and LaGuardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations.National Science Foundation (U.S.) (Grant 1239054

Estimation of maximum-likelihood discrete-choice models of the runway configuration selection process

The runway configuration is the subset of the runways at an airport that are used for arrivals and departures at any time. Many factors, including weather (wind and visibility), expected arrival and departure demand, environmental considerations such as noise abatement procedures, and coordination of flows with neighboring airports, govern the choice of runway configuration. This paper develops a statistical model to characterize this process using empirical observations. In particular, we demonstrate how a maximum-likelihood discrete-choice model of the runway configuration process can be estimated using aggregate traffic count and other archived data at an airport, that are available over 15 minute intervals. We show that the estimated discrete-choice model not only identifies the influence of various factors in decision-making, but also provides significantly better predictions of runway configuration changes than a baseline model based on the frequency of occurrence of different configurations. The approach is illustrated using data from Newark (EWR) and LaGuardia (LGA) airports

Estimation and tactical allocation of airport capacity in the presence of uncertainty

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 209-215).Major airports in the United States and around the world have seen an increase in congestion-related delays over the past few years. Because airport congestion is caused by an imbalance between available capacity and demand, the efficient use of available capacity is critical to mitigating air traffic delays. A frequently-adopted traffic management initiative, the Ground Delay Program (GDP), is initiated when an airport expects congestion, either because of very high demand or a reduction in its capacity. The GDP is designed to efficiently allocate the limited airport capacity among the scheduled flights. However, contemporary GDP practice allocates delays to arrivals independent of departures, and relies on deterministic capacity forecasts. This thesis designs and evaluates a GDP framework that simultaneously allocates arrival and departure delays, and explicitly accounts for uncertainty in capacity forecasts. Efficient capacity allocation requires the accurate estimation of available airport capacity. The first module of this thesis focuses on the modeling of airport capacity and its dynamics. A statistical model based on quantile regression is developed to estimate airport capacity envelopes from empirical observations of airport throughput. The proposed approach is demonstrated through a case study of the New York metroplex system that estimates arrival-departure capacity tradeoffs, both at individual airports and between pairs of airports. The airport capacity envelope that is valid at any time depends on the prevailing weather (visibility) and the runway configuration. This thesis proposes a discrete choice framework for modeling the selection of airport runway configurations, given weather and demand forecasts. The model is estimated and validated for Newark (EWR) and LaGuardia (LGA) airports using archived data. The thesis also presents a methodology for quantifying the impact of configuration switches on airport capacity, and applies it to EWR and Dallas Fort Worth (DFW) airports. The second module of this thesis extends two existing stochastic ground-holding models from literature, the static and the dynamic, by incorporating departure capacity considerations to existing arrivals-only formulations. These integer stochastic formulations aim to minimize expected system delay costs, assuming uniform unit delay costs for all flights. The benefits of the integrated stochastic framework are demonstrated through representative case studies featuring real-world GDP data. During GDPs, the Collaborative Decision-Making framework provides mechanisms, termed intra-airline substitution and compression, which allow airlines to redistribute slots assigned by ground-holding models to their flights, depending on flight-specific delay costs. The final part of this dissertation considers collaborative decision-making during GDPs in stochastic settings. The analysis reveals an inherent trade-off between the delay costs achieved by the static and the dynamic stochastic models before and after the application of the CDM mechanisms. A hybrid stochastic ground-holding model that combines the desirable properties of the static and dynamic models is then proposed. The performance of the three stochastic ground-holding models under CDM are evaluated through real-world case studies, and the robustness of the final system delay cost reduction achieved by the hybrid model for a range of operating scenarios is demonstrated.by Varun Ramanujam.Ph.D

Lane changing models for arterial traffic

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 126-129).Driving behavior models for lane-changing and acceleration form an integral component of microscopic traffic simulators and determine its value in evaluation of different traffic management strategies. The state-of-art model for lane changing adopts a two-level framework: the first level involves a latent or unobserved choice of a target lane; the second level models the acceptance of adjacent gaps in the direction of the target lane. While this modeling approach has several advantages over past works, it assumes drivers to execute lane change within the same time step in which gap was found to be acceptable. In other words, under time steps typically adopted in model applications, the lane change duration is assumed to be negligibly small. However, past works report average lane change durations to the order of 5-6 seconds. Besides this practical maneuvering requirement, the assumption fails further in moderate or low density traffic conditions with ample gap sizes or low speed conditions, where lane changing maneuver can take longer than average. The work outlined in this thesis proposes an extension to the two-level framework for lane changing models through a third level that explicitly models the lane change duration.(cont.) Traffic conditions in the driver's neighborhood that are likely to influence lane change duration are accounted for in the third level. The extended model is applied to data obtained from video observations on traffic on a stretch of an arterial corridor in California. Apart from possessing distinctive features including signalized intersections and multiple access locations that result in lower average speeds, the arterial dataset used in this study represents a relatively low density scenario in terms of gap availability, thereby presenting an ideal test-bed for the proposed model extension. Since arterial datasets have not received predominant attention in literature, this work uncovers some traffic aspects not encountered in past studies. The model is estimated using a sample of the overall dataset available in the form of disaggregate vehicle trajectories. The estimated model is implemented in a microscopic traffic simulator MITSIMLab, and model validation is done using aggregated traffic data. Estimation and validation results showcase the improved modeling capabilities achieved through the proposed extension.by Varun Ramanujam.S.M

Estimation of Arrival-Departure Capacity Tradeoffs in Multi-Airport Systems

Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009.The accurate estimation of airport capacity is critical for the efficient planning of landing and takeoff operations, and the mitigation of congestion-induced delays. The analysis of tradeoffs between arrival and departure capacity at an airport, represented by the airport capacity envelope, has been the main focus of prior research. The increasing demand for air traffic operations has resulted in the growth of multi-airport systems, in which several major airports that are in close proximity of each other serve the same geographical region. The arrival and departure flows into these airports interact with each other, and it is necessary to consider inter-airport arrival-departure capacity tradeoffs while scheduling operations. This paper proposes a statistical technique based on quantile regression, for systematically analyzing arrival-departure capacity tradeoffs in multi-airport systems using observations of flight operations. The proposed technique enables the identification of key factors (such as, runway configuration geometry, weather conditions, etc.) that influence both the capacity envelopes of individual airports, and the capacity envelope of the multi-airport system as a whole. The approach is demonstrated through an analysis of the capacity envelopes of the New York area multi-airport system (comprising Newark (EWR), John F. Kennedy (JFK) and LaGuardia (LGA) airports)

Estimation of maximum-likelihood discrete-choice models of the runway configuration selection process

The runway configuration is the subset of the runways at an airport that are used for arrivals and departures at any time. Many factors, including weather (wind and visibility), expected arrival and departure demand, environmental considerations such as noise abatement procedures, and coordination of flows with neighboring airports, govern the choice of runway configuration. This paper develops a statistical model to characterize this process using empirical observations. In particular, we demonstrate how a maximum-likelihood discrete-choice model of the runway configuration process can be estimated using aggregate traffic count and other archived data at an airport, that are available over 15 minute intervals. We show that the estimated discrete-choice model not only identifies the influence of various factors in decision-making, but also provides significantly better predictions of runway configuration changes than a baseline model based on the frequency of occurrence of different configurations. The approach is illustrated using data from Newark (EWR) and LaGuardia (LGA) airports

Data-Driven Modeling of the Airport Configuration Selection Process

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choice modeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial “nested logit” model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and LaGuardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations. National Science Foundation (U.S.) (Grant 1239054) Document type: Articl

Modeling Acceleration Decisions for Freeway Merges

In uncongested traffic situations, a merge is executed when the available gap is sufficiently large. However, in congested traffic, acceptable gaps for merging are often not readily available, and merging can involve more complex mechanisms. For example, the driver in the target lane may slow down and cooperate with the merging driver, or the merging driver may become impatient and decide to force in, and compel the lag driver in the target lane to slow down. Choices of the merging plan or tactic affect the gap acceptance and acceleration decisions of the driver. A driver who has decided to force in, for instance, is likely to accept smaller gaps and accelerate to facilitate the merge. The chosen tactic at any instant, however, is not distinctly observable from the vehicle trajectory. The model presented in this paper extends previous research in modeling the effect of merging plans in the lane-changing decisions by integrating the acceleration decisions of the driver with the gap acceptance decisions. A combined model for merging plan choice, gap acceptance, target gap selection, and acceleration decisions of drivers merging from the on-ramp is developed in that regard. Parameters of all components of the models are estimated jointly with detailed vehicle trajectory data collected from Interstate 80 in California. The inclusion of the target gap choice and acceleration behavior components has been supported by a validation case study in which the model has been implemented in MITSIMLab and validated against the observed aggregate traffic data collected from US-101 in California.Federal Highway Administratio