Flight Arrival Times Research Articles

Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area

The effectiveness of air traffic management depends on the amount of obtained information and the accuracy of prediction, leading to a reduction in uncertainty disturbances, and an improvement in the robustness of decision-making results. Among all controlled areas, the Terminal Manoeuvring Area (TMA) is a complex and sophisticated airspace surrounding a coordinated airport in which there exists a high density of traffic, especially with arrivals converging from different directions. With the purpose of improving safety levels and operational efficiency, this paper addresses the aircraft arrival scheduling problem at the microscopic level, integrating the meteorological uncertainty based on wind networking into the optimization model. Wind uncertainty affects the flight speed, which in turn leads to uncertainty in the flight arrival time at one waypoint. Conflict detection and resolution are performed at waypoints and links in a predefined network, based on the predicted time information from the designated route. In order to optimize the time and speed for each aircraft entering TMA, this paper proposes a mathematical model that seeks to minimize the number of potential conflicts, flight delays and the number of flights deviating from the original plan under two assumptions. The first one considers regular weather forecasts for predicting the future positions of aircraft for conflict detection. The second one investigates the potential benefit for aircraft to share their local wind measure thanks to the ADSB-IN and ADSB-OUT channels. A resolution approach is proposed combining a simulated annealing algorithm and a simulation framework. Using data from a case study with 437 flights overflying the Paris Charles de Gaulle (CDG) airport TMA, we demonstrate the validity of the proposed scheduling strategy. A performance analysis is then conducted between standard forecast and wind networking.

A Statistical Analysis Arrival Times at the Airport Using Chi-Square and Box Plots Analysis

In this report we will be investigating two datasets and calculating their statistical quantities for the interarrival times of incoming flights at DFW airport. This set contains the flight arrival time information of flights between roughly 6:00 AM and 12:30 PM on September 12th, 2022. The data was collected from the website by copying, pasting, and manually formatting the text provided by it. Our team believes that flights happened in a pattern which will follow an exponential distribution when graphed. We extrapolated and formatted the data based on the given flight times and presented it in tables and graphs to better visualize the data. While doing this has not confirmed our hypothesis, it shows promising similarities to our expectations and merits further testing. The main Grand Challenge that this and other datasets take on is restoring and improving urban infrastructure. The main SDGs it serves is industry, innovation, and infrastructure, as well as sustainable cities and communities. All the datasets can be used to advance infrastructure. Improving energy efficiency can lower expenses for everyone, lower emissions, and allow for other areas of infrastructure to be taken care of. Identifying trends in inter-arrival times will inform the global infrastructure to account for changes in travel demand, allowing business, leisure, and trade, etc. to continue with minimal problems and efficient spending.

Cooperative scheduling optimization for ground-handling vehicles by considering flights’ uncertainty

The ground-handling vehicle services of airports are facing significant challenges due to the uncertainty of actual flight arrival time, leading to low resource utilization efficiency and unreasonable service sequence. Moreover, there are different properties (service objects and service orders) of ground-handling vehicles that are not only challenging for modeling schedules of various vehicles but also increase the difficulty when dealing with coordination optimization among all different kinds of ground-handling vehicles. In this paper, the refuellers and shuttle buses would be integrated to provide a more realistic and coordinated optimization schedule for ground-handling vehicles’ dispatching. Specifically, multi-objective mixed integer programming is developed to optimize the priority of services and corresponding service time window by minimizing service costs and time losses. Then, the uncertainty of the flights’ actual arrival time is described by the method of chance constraints. Subsequently, the proposed model is utilized at the Beijing Capital Airport to indicate the effectiveness of the proposed modeling framework. Results demonstrate that significant service performance gains (reduced by 66% in time exceeding the planned departure time, 45% in extra waiting time of vehicles, 40% in flights not departing on schedule, and increased by 31% in the absorption of delay time, etc.), which demonstrates its application in solving schedule problems among other airports.

Robust Airport Gate Assignment Based on the Analysis of Flight Arrival Time

At present, hub airport is facing a lot of emergency situations, e.g., the management of irregular flight, which affects the sustainable development of the airport. Most of the methods for evaluating the robustness of airport gate assignment have been proposed by giving an independent evaluation function based on their experience, and thus, subjective factors are involved in varying degrees. Therefore, this paper puts forward an objective evaluation method based on the analysis of airport flight arrival, which gives a digital transformation strategy for the airport resource scheduling. A novel evaluation method based on data analysis of flight arrival time, and a mathematical model are formulated accordingly. In order to solve the proposed NP-hard problem, a new hybrid genetic algorithm combined with a heuristic algorithm is designed. In numerical experiments, the actual flight data of an international hub airport are used to analyze and verify the effectiveness of the proposed method. The results showed the advantages in scheme stability, operating cost savings, and risk reduction of the proposed method.

Stochastic terminal flight arrival and departure scheduling problem under performance-based navigation environment

Flight arrival and departure scheduling (FAADS) problem, one of the critical tasks in terminal air traffic operation, faces new challenges as the terminal air traffic management has transformed to adapt to the performance-based navigation (PBN) environment; beside of that, the terminal system uncertainties which are usually due to the time-varying interference of the convective weather and flight arrival time will also exacerbate the difficulty to realize an efficient terminal air traffic operation. In order to effectively address the above issues, we propose the formulation and solution approach for a stochastic terminal FAADS problem under PBN environment. The proposed FAADS problem formulation combines the flexible 4-dimensional trajectory requirement under PBN environment and the stochastic quantifications both from the convective weather and flight arrival time uncertainties, which can finally make the FAADS results realize the avoidance of the convective weather and immunity of the flight arrival time variations within tolerable risk probabilities. We provide an efficient solution approach to tackle this problem with complex mixed integer and nonlinear programming basics and illustrate the capabilities of the solution approach by a test case on real terminal system in Shanghai Metroplex. Numerical results demonstrate the effectiveness of proposed problem formulation and solution approach.

Analysis of short-term forecasting for flight arrival time

We suggest various methodologies to provide short-term forecasting of flight arrival times. Flights arriving at Denver International Airport from various U.S. cities during 2010 are used for the model estimation, and the forecasting is applied to 2011 flights. Forecasting proceeds from the time at which a flight departs from an airport. Prediction models using the spline smoothing-based nonparametric additive techniques are applied and compared with benchmarks. We also provide a method for computing the probability of flight arrival time by fitting the skew t distribution to the models’ residuals. Our empirical results indicate that a nonparametric additive model dominantly outperforms the other models considered. In terms of effect of predictor variables, departure delay time, scheduled airborne time, airlines, and weather conditions significantly improve forecasting accuracy, along with seasonal variables. In particular, departure delay time is the most important factor for substantially improving prediction performance.

A model enhancement heuristic for building robust aircraft maintenance personnel rosters with stochastic constraints

This paper presents a heuristic approach to optimize staffing and scheduling at an aircraft maintenance company. The goal is to build robust aircraft maintenance personnel rosters that can achieve a certain service level while minimizing the total labor costs. Robust personnel rosters are rosters that can handle delays associated with stochastic flight arrival times. To deal with this stochasticity, a model enhancement algorithm is proposed that iteratively adjusts a mixed integer linear programming (MILP) model to a stochastic environment based on simulation results. We illustrate the performance of the algorithm with a computational experiment based on real life data of a large aircraft maintenance company located at Brussels Airport in Belgium. The obtained results are compared to deterministic optimization and straightforward optimization. Experiments demonstrate that our model can ensure a certain desired service level with an acceptable increase in labor costs when stochasticity is introduced in the aircraft arrival times.