Journal of Air Transport Management最新文献

With the rapid increase in air traffic, the scheduling optimization of one single resource is difficult to meet the needs of airport surface operation. Thus, we propose a new joint scheduling model of airport gate and runway with three different objectives, i.e., service quality (minimizing the number of flights assigned to aprons), operation efficiency (maximizing the ground-air coordination) and environmental impact (minimizing the carbon emissions during the whole process of aircraft ground operation and airport noise disturbance). Then, we apply the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with an improved population initialization method to solve the model. Finally, we perform a case study based on Guangzhou Baiyun International Airport (CAN). The results show a negative correlation between operational efficiency and environmental impact. The optimized scheme can at most reduce 48 flights assigned to aprons, make all flights ground-air coordinated, or reduce 12.07t carbon emissions and 0.55 dB noise level at the runway end. Furthermore, we compare the median and minimum Pareto schemes to the original scheme. It is found that the model proposed in this paper optimizes not only the original assignment scheme on three objectives, but also the gate assignment robustness, runway usage balance, and other benefits.

The management of airline companies entails a multitude of critical decisions, with the selection of aircraft standing out as one of the most pivotal. This decision is notably crucial due to the substantial associated costs, amplifying its importance. To navigate such critical decisions with precision, businesses have increasingly turned to decision support systems, artificial intelligence applications, and analytical decision-making methods, aiming to minimize errors and optimize outcomes. This study aims to present an illustrative model by amalgamating the SWARA (Step-wise Weight Assessment Ratio Analysis) and COPRAS (Complex Proportional Assessment) methods, both falling under the umbrella of multi-criteria decision-making approaches. The specific focus is on the significant decision of aircraft selection within airline companies. The study identifies six criteria for assessment: purchase cost, fuel capacity, maximum seat capacity, range, maximum take-off weight, and cargo capacity. Upon scrutinizing the findings, it is evident that the rankings produced by the established mathematical model generally correspond with the preferences seen in the actual aircraft fleets of airline companies.

Network centrality is an intermediary between airport resource utilization and air traffic generation. A central position in the network with frequent and regular flights with hub nodes can boost air traffic by providing better accessibility, resulting in more efficient use of airport resources. However, this relationship has been largely ignored in the literature. Using data from the Turkish airport industry, this paper proposed a weight-restricted Network Data Envelopment Analysis model, which considers network centrality measures as the cornerstone intermediates that establish the link between airport resources and the traffic volume handled. In the first stage, called networkability, assets such as runways, terminals, aprons, and special purpose vehicles, and exogenous factors including population, socio-economic development, and tourist arrivals are used to accomplish the network integration with other airports, as measured by degree centrality, betweenness centrality, and eigenvector centrality. In the second stage, called traffic generation, this network integration allows for aircraft movements and workload unit to be handled. Criteria weights of model variables were calculated using Criteria Importance Through Intercriteria Correlation. The main findings indicate that 1) the weight-restriction procedure improved the robustness of Network DEA, 2) the proposed two-stage structure reveals whether performance losses are due to networkability or traffic generation capabilities and helps to identify the right policies for performance improvement, 3) the Turkish airports generally suffer from the inability to establish connections in the domestic network, 4) the pandemic has significantly improved the domestic networkability of airports due to mandatory direct flights while devastating the traffic generation capability, 5) low betweenness centrality is the main reason for weak networkability, and 6) good networkability may not ensure air traffic generation.

Aircraft noise models are fundamental tools for noise abatement, control, enforcement, evaluation, and policy-making. Validation of aircraft noise models is necessary to ensure their reliability and credibility, particularly given their significant impact on society, the economy, and public health. However, validating such models is often a complex undertaking, and an acceptable validation methodology still needs to be developed. In this study, the Federal Aviation Administration's (FAA) Aviation Environmental Design Tool (AEDT) aircraft noise model is validated by correlating the calculated and measured noise levels for a specific aircraft flying in a particular flight path at Heathrow Airport. The validation results suggest that the AEDT noise model estimates the actual noise level quite accurately for landings, with a variation less than 2 dB(A), but might be inaccurate for takeoffs for certain aircraft types, with variations reaching 10 dB(A), resulting in a considerable difference between the measured and calculated noise levels.

This paper employs a novel multivariate panel Granger causality approach to examine the relationship between the 2019 coronavirus disease, Delta and Omicron pandemic and 14 air transport companies and airports during a certain time period, taking into account both the interdependence and heterogeneity among these air transport companies and airports. Empirical results show that 10 out of 14 air transport companies and airports have a one-way direction of Granger causality between pandemic shocks and stock returns, among which five air transport companies and airports have bilateral relationships. The results show that six air transport companies and airports are Granger “leading” the pandemic, arguing that the adjustment speed of expectations of exogenous shocks and the policies may account for the counterintuitive causal relationship which brings new insights into the heterogeneity in expectations.

The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.

Airline companies try to increase their revenues, service level, and customer satisfaction in a highly competitive global sector. Airline schedule planning is crucial for airline companies to reach these objectives. Airline schedules are usually constructed assuming that there will be no disruption. But in reality, there are plenty of incidences such as weather conditions, mechanical failure, air traffic, and security issues that cause delays and disrupt daily operations. Even though it is impossible to avoid the delay completely, there are ways to decrease the propagation of the delay. To cope with delay propagation, airlines insert idle time, known as slack, between flights in the schedule. However, idle time means inefficient use of aircraft resources. Thus, adjusting the idle time in the schedule dynamically during daily operations is a critical task for planning departments. In this study, flight time rescheduling and aircraft swapping are used to decrease the expected delay propagation. By using these two options, the scheduled slack is clustered at flights that are prone to delay propagation. We aim to reduce the negative consequences of delay proactively while keeping the total slack constant in the schedule. Keeping the slack constant helps reduce other adverse network effects and enables the rest of the plan to be still intact for the future. We propose to use multivariate kernel density estimation to estimate the probability of independent delay from flight data and argue that this is a practical and effective way of estimating such distributions for daily airline operations. We use that estimation in two mathematical programming formulations: the single layer model, and the single layer model with aircraft swapping option to minimize the expected propagated delay. Since the latter model is a non-linear model, we also introduce an approximation for it to overcome the computational issues in solving large instances of the problem. After illustrating our approach on a small set of data, we report our computational results using flight schedule data from Turkish Airlines augmented with weather related information. We argue that the proposed models help decrease the expected delay propagation by up to 90% allowing a 15-min change in the schedule and swapping aircraft when necessary.

With the increasing demand in operations, time is getting more important. In order to use time and energy more effectively, it is becoming more important for airline companies and airport managements to make strategic plans for the future. To make beneficial and correct strategic plans for airways, one of the factors that is needed to be considered is future passenger numbers. With more accurate passenger number forecasts, airport managements can act more efficiently and reduce time, energy consumption and hence would be able to reduce costs. In this study, airway passenger number estimation is handled. Three metropolitan cities’ airport passenger numbers are considered. Artificial neural networks and regression analysis are carried out to estimate passenger number. In addition, data are handled in two different ways. Firstly, ANN and regression analysis are applied using original data series. In the second step, seasonal decomposition is applied on the data series and both approaches are repeated for deseasonal series. In Artificial Neural Networks approach, an experimental design is developed considering training algorithms, number of input nodes and number of nodes in the hidden layer which make up 960 design points. In the results of these experiments, performance of ANN approach is tested for three input factors and high-performance design points are identified. Furthermore, for benchmarking purposes, regression analysis is carried out. Linear, logarithmic, power, exponential, and polynomial models are developed. Finally, results of ANN and regression approaches are compared in terms of mean absolute percent error, and it is found that ANN overperformed compared to regression analysis.

Schedule design in the transportation and logistics sector is a widely studied problem. Transport service providers, such as the train industry and aviation, aim for schedules to be on-time according to the planning (i.e., on-time performance or OTP) in order to increase the service level by ensuring that passengers actually make their connections and to reduce costs. Transportation services also aim for schedules that serve a high variety of destinations and frequency of connections (i.e., connectivity). OTP and connectivity are both highly dependent on buffer time: more lucrative connections can often be offered by reducing the buffer time in the schedule, while more delay can be absorbed by more buffer time. Given strict constraints on the minimum turnaround time of aircraft and minimum (and maximum acceptable) transfer times of passengers, assigning buffer time in an already tightly planned schedule to optimize OTP and connectivity simultaneously is a big challenge. This research presents a novel multi-objective formulation of a daily flight schedule where buffer scheduling is used to ensure the optimal balance between OTP of the schedule and the passenger connections as connectivity, given the tight restrictions. This problem formulation is solved using a simulation–optimization framework. Specifically, we use the Multi-Objective Evolutionary Algorithm (MOEA) BORG. As a proof of concept, a daily European flight schedule of a large international airline is optimized on both OTP and connectivity. The results demonstrate that the presented multi-objective formulation and associated solving through simulation–optimization can result in candidate schedules with both better on-time performance and a higher connectivity.