Scheduled Commercial Flights Research Articles

Demonstration of Fixed Flight-Path Angle Descent via Scheduled Commercial Flights

Toward implementation of fuel-saving descent operations, this study demonstrated a fixed flight-path angle descent using regularly scheduled commercial flights arriving at Kansai International Airport. This paper summarizes the authors' industry/academia collaboration research including 1) operational bottlenecks and weather analysis, 2) proposals of pilot and air traffic controllers’ operational procedures, 3) validation experiments using an A320 flight simulator, and 4) flight demonstrations conducted across 24 days using A320neo and A320ceo aircraft. The flight demonstration results were evaluated comparing flight recorder data of the new descent procedure relative to current descent operations. A total of 92% of the flights were approved to fly new approach procedure under actual air traffic operations, with an approximate average of 150 lb fuel reduction per flight (230 lb maximum).

Flight departure delay forecasting

The most accurate methods to predict delays have become essential to mitigate their generation and proliferation (and prevent their spread to other airports in the network). Given that both the delay time dependence function and the input variables on which it depends are not fully known, the objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. The novel contribution of this work is, first, to make a comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented (ten in total) and, secondly, to determine the coefficients of the importance of the features or flight attributes. Using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. The data for the analysis was obtained from the Colombian airport system for the year 2018. From the results obtained, it is worth mentioning that the model that presents the best performance is the Ensemble, or combination method of Random Forest Regressor models, configured with 2,000 trees within the forest, with an acceptable prediction range measured with the root-mean-square-error (RMSE) metric of 16 to 33 minutes (prediction of time of the flight departure delay) depending on the scenario analysed.

Prediction of departure flight delays through the use of predictive tools based on machine learning/deep learning algorithms

Abstract The objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. Since the novel contribution of this work is, first, to make the comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented and, second, to determine the coefficients of the importance of the features or flight attributes, using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. From the results obtained, it is worth mentioning that the model that presents the best performance is the ensemble or combinatorial method of random forest regressor models, with an acceptable prediction range (measured with the root-mean-square-error).

The evolution of Cuba's air transport network in the Embargo Era: 1967–2017

Beginning in 1961 the United States government enacted a series of laws that embargoed, or prohibited, trade and travel between the U.S. and Cuba. Because of these actions, from 1962 until August 2016 there were no regularly scheduled commercial flights between the United States and Cuba. With improvement in relations under the Obama administration, U.S. airlines were given the green light to resume such operations. This renewed relationship has created an opportunity to examine the changes in Cuba's air transport network during the first 50 years of the embargo. This paper examines the evolution of Cuba's air transport network after losing connectivity with the U.S., and attempts to establish a baseline for future research that evaluates the air network changes resulting from regained U.S. connectivity. The paper hypothesizes that the U.S. embargo inadvertently helped create a broader air transport network for Cuba because the island was forced to seek new alliances and sources of revenue. This hypothesis is evaluated by reconstructing Cuba's air transport network using historical airline schedules between 1967 and 2017, and analyzing changes in air transport links, gateway access, and capacity. Results point to a more restricted air transport network up until the collapse of the Soviet Union; and a period of development characterized by a wider international reach, deeper penetration at home, and the internationalization of Cuba's capacity.

Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model

This paper presents a machine learning approach to evaluate the performance of aircrafts using on-board sensor information on commercially scheduled flights with the aim to further improve system health monitoring strategies in air transportation. Logarithmic multivariate Gaussian models are trained to evaluate the performance of aircrafts at different flight phases (takeoff, ascent, cruise, etc.) separately. By including a forward synchronization, feature selection, and mini-batch training process, this model overcomes challenges introduced by the large size and high dimensionality of flight datasets. This framework also addresses the re-sampling issue in existing literature causing difficulties in handling time-series signals with different lengths. For demonstration and validation, the developed model is applied to analyze performance anomalies associated with the mechanical system and pilot operation in a historical flight dataset. Compared with existing literature focusing on similar datasets, this evaluation methodology shows promise in detecting performance anomalies especially at approach and takeoff phases. Therefore, the developed model is expected to be an effective addition to the current anomaly analysis and monitoring technologies for scheduled commercial flights. Applications include assisting transportation management systems by handling large amounts of historical flight datasets to analyze mechanical and operational anomalies, which may potentially improve future aeronautical system design and pilot training.

Aviation industry hopes to cut emissions with jet biofuel

This year, renewable jet fuel took off, graduating from demonstration and test flights to some regular commercial use. In January, some flights left the airport in Oslo, Norway, running on jet biofuel produced from an oilseed crop. In March, United Airlines became the first U.S. airline to use biofuel for regularly scheduled commercial flights leaving Los Angeles International Airport. Three months later, Alaska Airlines flew commercial flights using biofuel produced from renewable isobutyl alcohol. By 2050, the global aviation industry aims to combat climate change by reducing net carbon emissions by 50% compared with 2005 levels. That’s a commitment to cut one-tenth the emissions projected for 2050. Improved engine efficiency and aircraft aerodynamics will provide some reductions. But transitioning to fully renewable jet fuel is key to meeting the targets suggested by the International Air Transport Association (IATA). Alternative energy sources considered for cars and trucks—things such as batteries, fuel

Concorde supersonic transport noise monitoring program

The operational specifications of Air France and British Airways were amended to allow limited scheduled commercial flights of the Concorde supersonic transport into the United States for a trial period not to exceed 16 months. An interagency steering group comprised of representatives from the Secretary, National Aeronautics and Space Administration (NASA), Environmental Protection Agency, and FAA developed plans for noise, sonic boom, community response, and low-altitude pollution monitoring. Measurements have been taken since initiation of Concorde operations into Dulles Airport on 24 May 1976. Two community opinion surveys around Dulles Airport have been completed and the first JFK opinion survey was started on 1 February 1977. Results of the first two Dulles area surveys indicate that fewer people now disapprove of the Concorde than at the time of initiation of operations. Later surveys will provide results which may be compared to the first survey to detect changes in attitudes. A content analysis of print and broadcast media is being conducted to determine their treatment of the Concorde issue. Structural vibration studies conducted by NASA at Sully Plantation and in typical residences showed that Concorde caused more vibrations in the structures than other aircraft but not as much as nonaircraft events such as doors closing.

Medical Criteria for Passenger Flying on Scheduled Commercial Flights

Medical Criteria for Passenger Flying on Scheduled Commercial Flights

Medical Criteria for Passenger Flying on Scheduled Commercial Flights

Medical Criteria for Passenger Flying on Scheduled Commercial Flights