Journal of Air Transport Management最新文献

This systematic review identifies Australasian aviation climate change hazards to guide evidence-based climate risk management for the Australasian aviation industry. Identifying evidence-based climate hazards is imperative to inform local adaptation strategies. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, literature from 2005 to 2023 was searched and a qualitative systematic analysis of results undertaken. The search identified 22 records, including grey literature, and showed climate change hazards to flight operations include changes in wind, turbulence, dust, smoke, icing and hail. Hazards to airport operations include changes in precipitation, heat, saltwater inundation, tsunamis, lightning and volcanic ash. A first pass risk assessment was conducted to prioritise these climate change hazards to further guide industry risk management. In response, the Australasian aviation industry needs to introduce evidence-based climate risk management systems and disparate climate literature transferred to the aviation knowledge base. Research from the northern hemisphere needs to be adapted and contextualised to the Australasian setting where feasible, or replicated to meet specific regional needs, enhancing the climate resilience of the local aviation sociotechnical system.

Situational awareness (SA) and fatigue management are crucial aspects of aviation safety, particularly during demanding flight phases. This study introduces an innovative approach employing flight data, machine learning, and Continuous Performance Test (CPT) metrics to predict pilot performance and SA during instrument approaches under Instrument Meteorological Conditions (IMC). Data were collected from over 10 pilots across more than 40 flights in a high-fidelity Cessna 172 analog flight simulator.

Significant correlations were observed between dynamic cognitive performance parameters and the exceedance shape factor, a novel measure of pilot sustained attention introduced in this research. Key variables identified through correlation analysis included variability, interstimulus change, and reaction time standard deviation.

Importantly, commission scores and reaction time standard deviation emerged as key predictors in the machine learning model, specifically the Optimizable Gaussian Process Regression (GPR) model with a radial basis function kernel. The model achieved a validation R-squared of 0.90 and a test R-squared of 0.70. These systems could incorporate additional data sources, such as eye-tracking and scan pattern analysis, for a better assessment of pilot SA and fatigue levels. While post-flight measurements are inherently reactive, they are effective for monitoring the degradation of pilot CPT scores after each leg of high-frequency, short-duration flights.

Notable limitations include the need to understand individual cognitive differences among pilots, such as age, experience, and cognitive style. The predictive model also requires validation in actual flight conditions to determine its ecological validity. Future research should aim to address these limitations, generalize the findings, and integrate CPT data with other sensor inputs to provide a more comprehensive understanding of pilot performance.

In this study, the energy and emissions, as well as the capacity utilization and inefficiencies of global airlines in alliance and nonalliance groups, are analyzed using two-stage multiproduct network technology with a nonconvex metafrontier framework. By integrating group frontier and metafrontier analysis, our proposed model allows us to estimate both the constrained technology and unconstrained capacity gaps among airlines operating with different technologies. We examine the simultaneous effect of capacity utilization, energy and CO₂ emissions on global airlines using a metafrontier framework. The empirical results indicate that 12 airlines operate inefficiently at the constrained metafrontier, which may be due to group frontier inefficiency, technology gap inefficiency or both. In terms of network capacity utilization inefficiency, 6 airlines are required to scale labor when operating at maximum capacity. Several strategies are recommended to improve metatechnology technical efficiency and network capacity utilization.

Air transportation stands as an indispensable pillar of a city's economy. An effective and reliable air transport service plays an important role for the prosperity of a city. Moreover, in many cases, a city has multiple airports within its catchment area and the collaborative relationships under disruptions between these airport services have often been overlooked in prior studies. To bridge this gap, this paper firstly introduces the concept of “instant air accessibility” for a city and develops a resilience metric aimed at quantifying the impact of airport disruptions on a city's air accessibility, taking into account the perspective of a multi-airport system. We apply the metrics to a network with 48 cities at or above the second-tier level in China. Results of the air accessibility analysis show that some relatively small cities have high accessibility that are comparable to megacities in China, but some provincial capital cities have low accessibilities, although they maintain superior political and economic status among the cities. Most of the cities' accessibility are vulnerable to targeted disruptions. Additionally, we identify critical airports that wield significant influence over the overall accessibility performance of the entire network. The findings from this study offer valuable insights for the management of air transport resources and the enhancement of the resilience of cities' air accessibility.

Airports featuring multiple runways have the capability to operate in diverse runway configurations, each with its unique setup. Presently, Air Traffic Controllers (ATCOs) heavily rely on their operational experience and predefined procedures (”playbooks”) to plan the utilization of runway configurations. These ’playbooks’ however lack the capacity to comprehensively address the intricacies of a dynamic runway system under increasing weather uncertainties.

This study introduces innovative methodologies for addressing the Runway Configuration Management (RCM) problem, with the objective of selecting the optimal runway configuration to maximize the overall runway system capacity. A new approach is presented, employing Deep Reinforcement Learning (Deep RL) techniques that leverage real-world data obtained from operations at Philadelphia International Airport (PHL). This approach generates a day-long schedule of optimized runway configurations with a rolling window horizon, until the end of the day, updated every 30 min.

Additionally, a computational model is introduced to gauge the impact on capacity resulting from transitions between runway configurations which feedback into optimized runway configurations generation. The Deep RL model demonstrates reduction of number of delayed flights, amounting to approximately 30%, when applied to scenarios not encountered during the model’s training phase. Moreover, the Deep RL model effectively reduces the number of delayed arrivals by 27% and departures by 33% when compared to a baseline configuration.

To the best of the authors' knowledge, research predicting airline passengers' satisfaction based on their sentiments and ratings is seldom sighted. Additionally, the literature reveals that most studies have primarily concentrated on specific airlines or routes, neglecting to conduct a comparative analysis of satisfaction levels across numerous airlines and routes. Hence, this research aims to predict passengers' satisfaction by combining the sentiment of their reviews and ratings on various parameters like food, entertainment, seat comfort, ground service, and value for money. Using the "Skytrax Airline Reviews" dataset, which contains data about 81 airlines and 64440 reviews, our research analyzes and predicts airline passengers' satisfaction based on sentiments and ratings using nine popular machine learning techniques. The study found that the LightGBM obtains an accuracy of 97 percent in predicting customer satisfaction. The results further reveal that 'value for money' and 'ground service' are crucial factors in determining the passengers' satisfaction, whereas 'entertainment' had no significant impact. Our study thus provides a valuable tool for predicting airline industry customer satisfaction and gives insight into the factors contributing to passenger satisfaction. These findings can further help airlines better understand their passengers' needs and improve their services accordingly.