Arrival Flights Research Articles

Comprehensive Evaluation of Airspace and Ground Operation Simulation in Large-Scale Airport

The evaluation model of airport airspace and ground operation was constructed by using analytic hierarchy process (AHP). 5 airspace operation indexes were selected: potential impact to military aviation, airspace demand range, number of arriving and departure routes, flow equilibrium of entry and exit point, arrival air delays time. 7 evaluation indexes of ground operation were selected: number of daily departure and arrival flights, equilibrium of runway usage, departure ground delays time, ground taxiing time, usage equilibrium of main taxiway, turnover frequency of contact gate, air bridge ratio. The index scale method was used to determine the weight of each index, which had better ordering, consistency and uniformity. Taking Beijing New Airport as an example, the airspace and ground operational efficiency was evaluated comprehensively. The results show that the “3+1” runway configuration is better than “4+0”.

MAIN REASONS FOR FLIGHT DELAYS

For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version).ABSTRACT The author considers one of the most topical issues in the field of air transportation - factors contributing to emergence of delays of flights. The author suggests analysis and classification of delays in the process of arrival and departure of flights and of the reasons that most often lead to a violation of regularity of flights. The concept of punctuality in the context of work of an airport and personnel according to a schedule is explored. The types of punctuality are suggested and analyzed from the point of view of timeliness of departure of flights, accuracy of arrival of flights, compliance with schedules of taking-off and landing. The method of calculation of all types of punctuality is given. The final part of the article assesses the international experience of monitoring of the regularity of air traffic of passenger aircrafts, airports and airlines. Keywords: air transportation, airport, airline, flight, delay, punctuality, classification of delays, calculation of punctuality, slot.

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management.

Base of Aircraft Data–based operating cost prediction of arrival flight delay under short-term weather

In the pre-tactical phase of air traffic flow management, flights in terminal airspace generate plans, such as rerouting and holding, as influenced by the weather. Hence, the new flight rerouting a...

Multinomial Logistic Regression Model for Predicting Flight Arrival & Delay

Multinomial Logistic Regression Model for Predicting Flight Arrival & Delay

Wait Time Prediction for Airport Taxis Using Weighted Nearest Neighbor Regression

In this paper, we address the neighborhood identification problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time prediction for taxi drivers at airports and investigate heterogeneous factors related to time, weather, flight arrivals, and taxi trips. The neighborhood-based methods have been applied to this type of problem previously. However, the failure to capture the relevant heterogeneous contextual factors and their weights during the calculation of neighborhoods can make existing methods ineffective. Specifically, a driver intelligence-biased weighting scheme is introduced to estimate the importance of each contextual factor that utilizes taxi drivers’ intelligent moves. We argue that the quality of the identified neighborhood is significantly improved by considering the relevant heterogeneous contextual factors, thus boosting the prediction performance (i.e., mean prediction error < 0.09 and median prediction error < 0.06). To support our claim, we generated an airport taxi wait time dataset for the John F. Kennedy International Airport by fusing three real-world contextual datasets, including taxi trip logs, passenger wait times, and weather conditions. Our experimental results demonstrate that the presence of heterogeneous contextual features and the drivers’ intelligence-biased weighting scheme significantly outperform the baseline approaches for predicting taxi driver queue wait times.

The Relationship between Airport Infrastructure and Flight Arrivals in Remote Northern Canadian Communities

Much of Canada’s northern population resides in communities that are inaccessible by road for a substantial portion of the year. Residents of these “fly-in” communities rely on aircraft to provide a wide range of social, economic, and transportation services. However, for numerous reasons, including the often extreme environmental conditions in the circumpolar regions of Canada, a substantial number of flights to these communities are cancelled or diverted. Using a dataset from two airlines that serve the western portion of this region with information about schedules, delays, and cancellations of more than 18 500 flights, we examined the links between airport infrastructure, flight arrival reliability, and a variety of socioeconomic variables in 23 northern communities. Results show that runway length has a significant impact on the reliability of flight arrival, but also that the reliability of flights may not affect the cost of food in the communities included in our analysis. These findings provide evidence that lengthening runways could improve air service in the Canadian North.

Airline delay management problem with airport capacity constraints and priority decisions

This paper deals with the Airline Delay Management Problem (ADMP), which can be described as the task of dealing with daily airline operational delays and deciding whether to delay subsequent flights at a hub airport or to have them departing on time. An innovative integer linear programming approach is presented to the capacitated case of the ADMP and airport limitations in terms of bay availability, taxiway capacity and runway separation are incorporated to represent capacity constraints. Fuel cost, passenger compensation, and passenger inconvenience costs are included in the objective function. The decision variables include the re-timing of flight departures and arrivals, the use of the airport capacity over time and the rebooking of passengers in case of missed connections. To guarantee the linearity of the optimization model and fast computational times, a receding horizon modeling framework is adopted. The approach is applied to a case study using real operational and passenger data from an international hub-and-spoke carrier. The case study shows the capability of the linear model to deal with a complete day of operations within a few minutes. The results suggest that the proposed approach can lead to cost reductions of almost 30% during recovery, when compared with the solution from the airline. In addition, a sensitivity analysis is provided to investigate the impact of not including passenger inconvenience costs and of reducing runway capacity.

Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.

This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.

Research on a robust multi-objective optimization model of gate assignment for hub airport

Gate assignment is one of the important tasks in the airport. The goal is to appoint an appropriate gate for the arrival or departure flight and ensures the flights are on schedule. So an appropriate and efficient gate assignment model can effectively decrease the flight delays and improve operation efficiency as well as service quality since it plays a major role in increasing revenues. In this paper, the multi-commodity network flow model is analyzed. And a robust multi-objective optimization model of gate assignment based on the objective of minimizing total time for passengers and balancing idle time for each gate is proposed to improve the service level and satisfaction of passengers and utilization rate of gates. The weighting method of linear is used to translate the multi-objective optimization model into the single objective optimization model. And ILOG CPLEX optimizer is used to solve the optimization model of gate assignment in order to obtain the reasonable gate assignment result. The schedule data of 116 flights from Sheyang Taoxian International Airport of China are used to validate the effectiveness of the constructed optimization model. And the multi-objective optimization model is compared with the existing mathematical model of gate assignment. The calculation and comparison results show that the constructed optimization model can significantly improve the assignment efficiency and satisfaction, and balance the utilization rate of gates. So the constructed optimization model of gate assignment can effectively provide a valuable reference for assigning gates.

P ea CE-Ful Web Event Extraction and Processing as Bitemporal Mutable Events

The web is the largest bulletin board of the world. Events of all types, from flight arrivals to business meetings, are announced on this board. Tracking and reacting to such event announcements, however, is a tedious manual task, only slightly alleviated by email or similar notifications. Announcements are published with human readers in mind, and updates or delayed announcements are frequent. These characteristics have hampered attempts at automatic tracking. P ea CE provides the first integrated framework for event processing on top of web event ads, consisting of event extraction, complex event processing, and action execution in response to these events. Given a schema of the events to be tracked, the framework populates this schema by extracting events from announcement sources. This extraction is performed by little programs called wrappers that produce the events including updates and retractions. P ea CE then queries these events to detect complex events, often combining announcements from multiple sources. To deal with updates and delayed announcements, P ea CE’s schemas are bitemporal, to distinguish between occurrence and detection time. This allows complex event specifications to track updates and to react upon differences in occurrence and detection time. In case of new, changing, or deleted events, P ea CE allows one to execute actions, such as tweeting or sending out email notifications. Actions are typically specified as web interactions, for example, to fill and submit a form with attributes of the triggering event. Our evaluation shows that P ea CE’s processing is dominated by the time needed for accessing the web to extract events and perform actions, allotting to 97.4%. Thus, P ea CE requires only 2.6% overhead, and therefore, the complex event processor scales well even with moderate resources. We further show that simple and reasonable restrictions on complex event specifications and the timing of constituent events suffice to guarantee that P ea CE only requires a constant buffer to process arbitrarily many event announcements.

Flight gate assignment and recovery strategies with stochastic arrival and departure times

We consider the problem of assigning flights to airport gates. We examine the general case in which an aircraft serving a flight may be assigned to different gates for arrival, parking, and departure processing. The objectives can be divided into deterministic and stochastic goals. The former include maximization of the total assignment preference score, a minimal number of unassigned flights during overload periods, and minimization of the number of tows. A special focus lies on the stochastic objectives, which aim at minimizing the expected number of any kind of constraint violations, i.e. not respecting gate closures, violation of shadow restrictions (a situation in which gate assignments may cause blocking of neighboring gates) or of tow time restrictions and classical gate conflicts in which two aircraft are assigned to the same gate and are at the airport at the same time. We show that the minimization of expected gate conflicts can be modeled in a graph theoretical approach using the clique partitioning problem (CPP). We furthermore show that the classical (deterministic) flight gate assignment problem, which can also be modeled using a CPP, can be integrated such that a simple though powerful model emerges, which no longer needs including a dummy gate, which is often used in practical gate assignment models. As constraint violations cannot fully be prevented, recovery strategies become necessary. We present a procedure for recovery planning that has proved its practical relevance at numerous airports. Finally, in an extensive numerical study we test our results on practical data, which contain a statistical analysis of flight arrival and departure times. The tests include a detailed comparison of current robustness measures and state-of-the-art approaches found in literature.

Can we measure the impact of new navigation procedure implementation? A simultaneous equations model approach

The Next Generation Air Transportation System or ‘NextGen’ is a series of programmes designed to transition the National Airspace System from a radar-based, air traffic controlled system to a satellite-based, air traffic managed system. NextGen programmes aim to improve the efficiency and predictability of gate-to-gate operations. They are also designed to support capacity management at large, congested airports and to foster better access in and out of busy metropolitan areas where the proximity of commercial and general aviation airports may create some challenges for air traffic controllers in approach and departure paths. This paper measures changes in four endogenous delay-related variables based on flight plan (on-time gate arrival, airborne, taxi-out and block delays) that affect different phases of a flight (arrival, en route, surface movement and gate-to-gate) when ceiling and visibility are in instrument meteorological conditions. Because of the interdependence of the variables in explaining performance outcomes, this study compares an ordinary least squares model with two-stage and three-stage least squares models to evaluate changes at Houston George Bush Intercontinental Airport (IAH) after the implementation of new procedures and routes on 29th May, 2014. Average excess miles flown and gate arrival delays represented the impact multipliers that affected the most the four endogenous variables before and after the implementation of new procedures and routes.

国内定期旅客便の運航効率の客観分析

Flight efficiency of passenger aircraft scheduled flights is analyzed by using surveillance radar data. Flight parameters which are necessary to evaluate the flight efficiency such as air speed and fuel flow are estimated from position data of the radar data by combining weather prediction data and aircraft performance models. Trajectory optimization is applied to the estimated flights in order to evaluate the flight efficiency by comparing with the minimized fuel and flight time. Arrival flights to Tokyo International Airport are analyzed to demonstrate the approach.

Estimation on LTO Cycle Emissions from Aircrafts at Civil Airports

A calculation method on pollutant emission inventory is established based on the standard LTO cycle of the International Civil Aviation Organization (ICAO) by analyzing the factors influencing aircraft engine emissions at civil aviation airports. For a certain airport in China, the emissions of HC, CO, NOx and SO2per hour for a whole day from the aircraft engines are calculated, and the variations of various pollutant emissions with time are analyzed based on the air traffic data, the civil aviation fleet composition, the flight detailed take-off and landing information at the airport, and ICAO engine emission data bank. It’s found that the variations of the pollutant emissions with time are different, in which, the emissions of HC and CO are significantly influenced by the frequency of flight arrival at airport, however, the emission of NOx is influenced by the frequency of flight departure from airport greatly, and the emission of SO2is influenced by the total frequency of flight arrival at and departure from airport comprehensively. For solving the problem of local high-emission time, some solutions are suggested, such as equipping aircrafts with low-emission engines or optimizing the flight schedule.

Optimised assignment of airport gate configurations using an immune genetic algorithm

The function of airport gate assignment is to assign appropriate gates for arrival and departure flights and to ensure the flights are on schedule. A key task of airport ground operations is assigning the airport gate with high efficiency and a reasonable arrangement. In this paper we establish an optimised model based on the characteristics of both the flights (the flight type, flight down time and the number of passengers) and the airport gates (the ease of access of the airport gate). We give a representation of the solution space and a direct graph model of the airport gate configuration based on dynamic scheduling parallel machines. We design a method for solving the airport gate configuration based on an immune genetic algorithm. The simulation results show the effectiveness of this model and algorithm.

Estimate of Dynamic Sector Capacity Considering Hazardous Weather

During the course of flight, we need to estimate regional sector dynamic capacity in order to assure the safety and efficiency of operation. Subjected to the controller workload and the decrease of CNS performance under the hazardous weather, and the controller workload is related to the degree of hazardous weather at the same time, the capacity on a route changing over time, the capacity of the associated sectors will also change. This paper, based on existing research, assumes a Poisson distribution flight arrival flow trends and hazardous weather in the coming period is known. It establishes a nonlinear programming model considering hazardous weather, controller workload and longitudinal interval between aircrafts innovatively, and then uses Matlab to solve the model. The results show that the model is feasible.

Aircraft Sequencing Method for Staggered Parallel Runways Approach

Most research about the approaching aircraft sequencing is for single runway approach or standard parallel runways approach. Little of research is for the case of staggered parallel runways approach. In this paper, a mathematical model of aircraft approach sequencing is established for the case of staggered parallel runways approach, and also this model is optimized with improved genetic algorithm. This model is simulated with the improved genetic algorithm both for cases of non-staggered runway approach and staggered runway approach, and the result shows that, comparing with the situation of non-staggered runway, the delay time in the case of staggered parallel runways approach decreases by 1.6 minutes, and the fitness increases by 0.0394. It proves that, in a certain extent, staggered parallel runways can reduce the minimum interval between arrival flights, thus increase the capacity of parallel runways.

Airport Capacity Enhancement and Flight Predictability

Justifications for enhancements to airport capacity are often framed in relation to flight delay reductions, but improvements to flight predictability also offer substantial benefits to the health of the aviation system. “Predictability” is defined in this paper as block time adherence and is measured as the difference between scheduled and actual block time. This research, using historical data, quantifies the impact on flight predictability of one airport's enhancement of infrastructure capacity. A case study using statistical methodologies, including cluster analysis of national airspace days and quantile regression of flights, identifies how deployment of a fifth runway at Hartsfield–Jackson Atlanta International Airport in Georgia affected the predictability of flight arrivals. The analysis identifies four scenarios—defined according to the level of national airspace strain and terminal airspace weather disruption—for which inclusion of the fifth runway in the runway configuration is associated with either improvement or degradation in predictability. If broad gains are to be made in improving predictability for the national airspace, then capacity enhancements may offer a limited contribution to what must be a multifaceted solution.

Experimental approach to NextGen benefits estimation: A case of single-airline Aircraft Arrival Management System

The Next Generation Air Transportation System (NextGen) embraces multiple operational and technological improvements that enable efficiencies for users and service providers. In managing the development of NextGen, the Federal Aviation Administration (FAA) is assessing initiatives and potential components of the future system. One component is software that jointly optimizes the timing of arriving traffic to streamline flight arrivals at congested airports (Aircraft Arrival Management Systems or AAMS). Since most of the proposed NextGen improvements are not yet implemented, their evaluations typically are conducted through modeling, simulation, and/or subject matter expert surveys. In contrast, this study utilizes experimental settings in a real-time operating environment to examine potential benefits of the AAMS. During the experiment, a number of system performance variables were recorded in two data collection periods: a passive period, when the system was operating without the AAMS, and an active period, when the AAMS optimized arriving traffic of a dominant airline. Due to confounding data issues, the results indicate that with only 6.5 percent of arriving traffic optimized, there are no observable improvements in the overall system performance. However, the study documents tangible benefits for optimized flights and a positive interaction effect between the FAA's Traffic Management Advisor and the AAMS.