International Flights Research Articles

Deep learning-derived optimal aviation strategies to control pandemics

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policies to mitigate the spread of infection, which led to economic crises as a collateral damage. In this work, we investigate the impact of human mobility, described via international commercial flights, on COVID-19 infection dynamics on a global scale. We developed a graph neural network (GNN)-based framework called Dynamic Weighted GraphSAGE (DWSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing flight information updated daily. This architecture is designed to be structurally sensitive, capable of learning the relationships between edge features and node features. To gain insights into the influence of air traffic on infection spread, we conducted local sensitivity analysis on our model through perturbation experiments. Our analyses identified Western Europe, the Middle East, and North America as leading regions in fueling the pandemic due to the high volume of air traffic originating or transiting through these areas. We used these observations to propose air traffic reduction strategies that can significantly impact controlling the pandemic with minimal disruption to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policymakers for making informed decisions regarding air traffic restrictions during future outbreaks.

Coordinated public health actions following the identification of a measles case arriving on an international flight, Spain: December 2022-January 2023

Contact tracing of individual exposed to any infectious measles case deserves special attention when in the country the disease is mostly imported. We present the coordinated public health actions triggered after reporting a suspected measles case in an aircraft. Spanish public health authorities and airlines responded promptly to allow a rapid contact tracing. Even one secondary measles case was reported no further transmission was identified, revealing that giving PEP and appropriate information help to avoid transmission and to maintain measles elimination in Spain.

SARS-CoV-2 Infection and Other Communicable Diseases Identified Among Evacuees From Afghanistan Arriving in Virginia and Pennsylvania, August to September 2021.

In 2021, the US government undertook Operation Allies Welcome, in which evacuees from Afghanistan arrived at 2 US ports of entry in Virginia and Pennsylvania. Because of the rapid evacuation process, the US government granted evacuees an exemption to a Centers for Disease Control and Prevention (CDC) requirement in place at that time-namely, that air passengers present a negative SARS-CoV-2 viral test result or documentation of recovery from COVID-19 before they boarded international flights bound for the United States. This study describes cases of SARS-CoV-2 infection detected among 65 068 evacuees who arrived at the 2 ports of entry in August and September 2021. Because evacuees were a population at increased risk for infection with diseases of public health concern, CDC staff helped coordinate on-site and on-arrival testing, visually observed evacuees for signs and symptoms of communicable disease, and referred evacuees for further evaluation and treatment as needed. CDC staff used antigen or nucleic acid amplification tests at the ports of entry to evaluate evacuees aged ≥2 years without documentation of recent SARS-CoV-2 infection. CDC staff isolated evacuees with confirmed SARS-CoV-2 infection and quarantined their close contacts, consistent with CDC guidance at the time, before evacuees rejoined the repatriation process. Of 65 068 evacuees, 214 (0.3%) were confirmed as having SARS-CoV-2 infection after port-of-entry testing. Cases of measles, varicella, pertussis, tuberculosis, hepatitis A, malaria, leishmaniasis, and diarrheal illness were also identified. Although the percentage of SARS-CoV-2 infection was low in this evacuated population, communicable disease detection at US ports of entry, along with vaccination efforts, was an important part of a multilayered approach to mitigate the transmission of disease in congregate housing facilities and into US communities.

Do healthy and environmentally sustainable inflight foods matter to international flight passengers? Frequent vs. occasional flyers

Airlines continuously seek strategic ways to impress their passengers and build memorable experiences. Inflight food services as an imperative interaction, the research included healthy and environmentally sustainable attributes (e.g., production method, product origin), reflecting the emphasis on longevity/well-being and sustainability to design optimal inflight food bundles as well as the brand and taste attributes. This exploratory research investigated differences in inflight food preferences between frequent and occasional flyers. A total of 16 full-profile pairwise comparison questions of mixed levels for five attributes were given to the sample of international flight passengers (n = 490). The participants were asked to rate inflight food bundles using a 9-point Likert scale. The results indicate that healthy and environmentally sustainable inflight foods will be preferred over brand-named or tasty foods when served onboard. There were notable differences in inflight food preferences between occasional and frequent flyers. While both flyers preferred low-calorie and tasty foods made with nationally sourced ingredients, frequent flyers highly preferred branded foods made with organically grown ingredients. In contrast, occasional flyers desired generic branded foods made with conventionally grown ingredients. The study findings are discussed further to help airline marketers build a desirable inflight food bundle.

Problems and Solutions of the Aviation Industry in the Post-epidemic Era

Three years after the outbreak, the year 2023 has officially entered the post-epidemic era. The aviation industry, which was severely hit by the epidemic, is gradually recovering. This article describes the current situation of the aviation industry after the epidemic, taking the Civil Aviation of China (CAAC) as the subject of the study, and analyses the problems it faces: the number of international flights has been slow to recover and unable to meet market demand; the financial crisis has been highlighted due to severe losses; supply chain shortages have prevented the timely delivery of aircraft; and the rapid increase in environmental pollution has led to a serious decarbonization situation. Potential solutions were also proposed for these four problems. These include increasing the number of flights through government consultation and communication, increasing revenue streams and reducing unnecessary spending to tide over the financial crisis, achieving supply chain resilience to avoid the obstacles posed by supply chain shortages, and developing new sustainable technologies for aviation to reduce air pollution.

FORECASTING NUMBER OF INTERNATIONAL TOURIST ARRIVALS USING MULTI INPUT INTERVENTION ARIMA MODEL

In 2020, the Covid-19 pandemic caused a very significant impact resulting in the drastic decline in the number of international tourist visits. As the Covid-19 pandemic ends, the government reopen international flight to Indonesia in early 2022 to remark the revival of the tourism industry. To determine how big the impact of the Covid-19 pandemic as well as the recovery process on international tourist visits through Soekarno-Hatta, Ngurah-Rai, and Kualanamu airports in the coming period, forecasting is needed. The forecasting method utilized in this study is multi-input intervention analysis. The first input is caused by the outbreak of Covid-19 pandemic, while the second input is due to the international flight reopening. The type of intervention variable chosen is a step function because both inputs give permanent effect to the international tourist arrivals. The data used in this study are monthly international tourist arrivals based on the entrances to Soekarno-Hatta, Ngurah-Rai, and Kualanamu International Airports from January 2008 to September 2023, taken from the Central Bureau of Statistics website. Based on the results, it was found that the number of international tourist arrivals entering Soekarno-Hatta airport can be modelled using SARIMA (0,1,1)(0,1,0)12 with (b=2, s=1, r=0) and (b=2, s=[3], r=0) for first and second input of intervention variable, respectively. Furthermore, the number of international tourist visits through Ngurah-Rai airport was more appropriate to be modelled using SARIMA (1,1,1)(0,1,1)12 with intervention inputs (b=1, s=[2], r=0) and (b=4, s=0, r=1). In Kualanamu airport, the first intervention order is equal to that in Ngurah-Rai airport, with (b=3, s=[3], r=0) for second intervention input and SARIMA (0,1,1)(1,1,1)12 for pre-intervention data. The forecast results show that the number of international tourist arrivals entering Soekarno-Hatta, Ngurah-Rai, and Kualanamu international airports are already recovered to pre-pandemic conditions at a quick pace

Paris 2024 Olympic Games: A risk enhancer for autochthonous arboviral diseases epidemics?

ObjectivesThe anticipated increase in international tourist flows and the first locally acquired dengue cases in the Paris region in October 2023 have raised concerns about potential arbovirus outbreaks during the 2024 Olympics. Unlike previous mass sporting events at risk of arbovirus outbreaks, Paris is a nonendemic arbovirus area, requiring a unique investigation. MethodsTherefore, we analyzed factors conducive to possible arbovirus epidemics in temperate regions: vector distribution in the Paris area, seasonal global arboviral disease patterns, projected visitor demographics, and international flight bookings. Results and conclusionOur results suggest that the expected visitors’ profile for the summer of 2024 should not increase the risk of arbovirus importation into the Paris region compared to a typical year. Conversely, the primary risk of arbovirus outbreaks is likely to come from within France, particularly from the French West Indies, where a notable, albeit declining, dengue outbreak is underway. Vigilant surveillance by French health authorities will ensure that this trend continues.

Factors influencing travelers' intention by environmental changes of destination: Cross‐country evidence from Far‐East Asia

AbstractTravel intentions are influenced by not only personal background but also external factors such as national disasters, infectious diseases, and government policy. While international flights and the travel market are sharply recovering from the effects of the COVID‐19 pandemic, travel risk will affect future travel demand. This study analyzed the relationship between potential risk of destination and travel avoidance intention using the stimulus‐organism‐response (SOR) model. The survey was conducted with 303 Chinese and 253 South Korean respondents living in countries adjacent to Japan. The results reveal that travel avoidance intentions may be influenced by emotional reactions to the complexity of the geopolitical environment or the diverse protection motivation perceptions of travelers. This study proposes that nationality has a moderate effect on tentative travel avoidance intention compared to gender.

Exploring the potential carbon emissions and net-zero path of international flights: A case study in Macao

With the rapid development of aviation industry and its increasing impact on the global climate change, the contributions of carbon emissions from international flights are attracting more and more attention worldwide. This study, taking Macao as the aviation hub, established the cross-border aviation carbon emission evaluation model to explore dynamic carbon emissions and net-zero path of international flights. The aviation hub mainly covers 58 routes and five types of civil aircraft from 12 countries or regions during 2000–2022. The results show that the aviation transportation in Macao emitted about 1.44 million tons CO2eq in 2019, which is high 3.6 times that of 2000. The COVID-19 has led to a rapid decline in aviation carbon emissions in a short period of time, carbon emissions in 2020 decreased by 80 % compared to 2019. In terms of cumulative carbon emissions from 2000 to 2019, the A321 and A320 Airbus contribute to 80 % of carbon emissions. And the Chinese mainland (37 %) and Taiwan (29 %) are the main sources of emissions. In 2000–2019, the proportion of carbon emissions from China (including Taiwan and Hong Kong) decrease from 91 % to 53 %, while the contribution from Southeast Asia (from 5 % to 26 %), Japan and South Korea (from 2 % to 19 %) keep the growth trends. In the optimal scenario (B3C3), net zero emissions of cross-border aviation in Macao can be not achieved, and there is still only by removing 0.3 million tons CO2eq. Emission reduction technology and new energy usage are priorities for the aviation emission reduction.

Integrating multiple data sources for improved flight delay prediction using explainable machine learning

Flight delays negatively impact costs, customer satisfaction, and revenue in the aviation industry. As a result, it is critical to identify the factors that cause flight delays for each airport, as they can vary depending on various attributes associated with their operations.This study proposes an explainable artificial intelligence (xAI) methodology for identifying the features that affect airport delays by integrating data from multiple sources and implementing explainable artificial intelligence. The methodology incorporates operational data, airport information, geographic data, and weather data combined and used to train a series of machine learning models. Furthermore, the SHAP and Sobol techniques are used to thoroughly analyze the features that influence flight delays for the specific case of the airport in Santiago, Chile.The results show that a linear discriminant analysis model is best suited for predicting flight delays in this specific case study, and the features that have the most significant impact on delays are the international flight status, average temperature at the destination airport, wind speed, and average temperature at Santiago airport.The proposed methodology could be applied by airlines that can collect data from multiple sources and conduct similar investigations, leading to the development of a decision support system to make better-informed decisions and reduce the impact of flight delays.

Analysis of the Potential of the Silangit International Airport in Increasing Tourist Visiting’s in the Danau Toba Region

This research aims to determine the potential of the Silangit International Airport to increase tourist arrivals in the Kawasan Danau Toba. This research is limited to the elements of international tourism accessibility since the opening of international flights Silangit Airport is expected to increase the number of foreign tourists visiting the Kawasan Danau Toba. This research uses quantitative and qualitative approaches using questionnaires and interview lists. The data collecting technique is purposive sampling by taking research subjects that fulfill the criteria. Data sources are Silangit Airport, immigration staff, transportation, hotels, restaurants, and foreign tourists. Data analysis used in this research was an analysis of frequency descriptions and interpretation of interview results. The results of the research found that there is great potential for Silangit Airport to increase the number of domestic and foreign tourist arrivals to Kawasan Danau Toba. This is indicated by the data processing results, which describe that all respondents stated that there was an increase in the number of foreign tourists arriving at the Silangit airport to visit as tourists and do business visits to the Kawasan Danau Toba.

Astronomy's climate emissions: Global travel to scientific meetings in 2019.

Travel to academic conferences-where international flights are the norm-is responsible for a sizeable fraction of the greenhouse gas (GHG) emissions associated with academic work. In order to provide a benchmark for comparison with other fields, as well as for future reduction strategies and assessments, we estimate the CO2-equivalent emissions for conference travel in the field of astronomy for the prepandemic year 2019. The GHG emission of the international astronomical community's 362 conferences and schools in 2019 amounted to 42,500 tCO2e, assuming a radiative-forcing index factor of 1.95 for air travel. This equates to an average of 1.0 ± 0.6 tCO2e per participant per meeting. The total travel distance adds up to roughly 1.5 Astronomical Units, that is, 1.5 times the distance between the Earth and the Sun. We present scenarios for the reduction of this value, for instance with virtual conferencing or hub models, while still prioritizing the benefits conferences bring to the scientific community.

Temporal variations in international air travel: implications for modelling the spread of infectious diseases.

The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption. The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel.

A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

Abstract Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

International flight fare prediction and analysis of factors impacting flight fare

In the rapidly evolving landscape of global travel, understanding international flight prices has become pivotal for both travellers and airlines. This paper delves into the intricate web of factors influencing flight prices, utilizing a dataset from Ease My Trip spanning 50 days. Employing rigorous data processing techniques, including handling missing values and label encoding, the study explores correlations between various parameters such as cabin class, flight numbers, airlines, and duration, shedding light on pricing dynamics. The research employs linear regression, decision trees, and random forest models for prediction. The results showcase the significance of class, flight numbers, and duration on prices. Particularly, higher cabin classes correlate strongly with increased prices, offering vital insights for airlines to optimize revenue. The models predictive accuracies are commendable, with the random forest model standing out, explaining 98.9% of the variance. This study not only illuminates the complex interplay of factors steering international flight prices but also provides airlines with robust pricing strategies. The findings empower travellers to make informed decisions, promising a harmonious future for the aviation industry in an ever-changing global market.

Analisis Pelayanan Staf Unit Lost and found dalam Menangani Permasalahan Delay Baggage pada Penumpang Singapore Airlines di Bandar Udara Internasional Juanda Surabaya

Delay baggage is one of the issues handled by the lost and found unit at the airport. This happens when the baggage arrives late at the destination airport, especially on international flights. Some factors that contribute to delay baggage are short connecting flight times, baggage label detachment, and loading errors. This research aims to find out how the staf at the lost and found unit handle delay baggage issues for Singapore Airlines at Juanda Surabaya International Airport. The research method used is descriptive qualitative with interviews, observations, and documentation as data collection methods. The data obtained from this research includes interview results with the staf at the lost and found unit, as well as documents used in their daily activities. The results show that the service provided by the staf at the lost and found unit to Singapore Airlines passengers experiencing delay baggage issues is in accordance with Standard Operational Procedure (SOP). This includes baggage search and retrieval, as well as compensation provided by the airline.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Human exposures to Brucella canis from a pregnant dog during an international flight: Public health risks, diagnostic challenges and future considerations.

This report documents the exposure of passengers and crew of a commercial international flight to the zoonotic pathogen Brucella canis after an infected dog aborted in the passenger cabin of the aircraft. This case demonstrates the challenges associated with brucellosis screening and the risks that airline personnel, airport employees and travellers face when animals with unrecognized zoonotic infections are transported. The public health investigation of this case was conducted by the Centers for Disease Control, the Illinois Department of Health and the Illinois Department of Agriculture, in collaboration with a local veterinary clinic and several academic and federal diagnostic laboratories. It included an extensive diagnostic evaluation of the dam and aborted foetuses to confirm a diagnosis of canine brucellosis. Passengers, airline personnel and staff from the veterinary clinic where the dogs were treated underwent risk assessments, and clinic staff also received detailed guidance regarding infection prevention practices. Animal shelters and breeding programs are recommended to screen dogs routinely for brucellosis, but it is not unusual for domestic or imported animals to have unknown health histories, including the dog's brucellosis status, at the time of purchase, adoption, or re-homing. Testing recommendations and requirements vary by state, making it challenging for state public health and animal health agencies to monitor and respond appropriately. This case highlights the importance of Brucella spp. screening in sexually intact dogs prior to breeding, purchase, or domestic or international transportation of the dogs. The transportation of pregnant dogs may present a previously unrecognized public health threat in addition to contributing to unnecessary stress and health risks for pregnant animals.

Processing of passenger information as an effective tool for combating terrorism

The article is devoted to the analysis of international and European practices of processing information about passengers in the context of combating terrorism. The provisions of UN Security Council resolutions N 2170 (2014), N 2178 (2014), N 2309 (2016), N 2396 (2017), N 2482 (2019), EU Directives N 004/82/EU, N 2016/681/EU, as well as standards of the International Civil Aviation Organization IKAO. Taking into account the requirements of these international and European acts, a comparative analysis of the draft law “On receiving, processing, distribution, storage and protection of information about passengers and personnel of vehicles of international flights” is performed. The optimal model, in the authors’ view, is proposed for implementation of the function of collecting information about passengers of all types of transport for the purpose of countering international terrorism, as well as preventing the movement of persons who may pose a threat to the national security of Ukraine.

The Physician in Flight: The Moral and Legal Responsibilities of Physicians When Present for In-Flight Medical Emergencies

A physician boards the plane and sits down. Does that physician face risk beyond a plane crash? If there is a medical emergency in-flight, does the physician face moral/ethical/legal risk by acts of commission or omission in relation to the medical emergency? That may depend on whether the flight is within the United States or is an international flight. This uncertainty prompts the question: Do we need broader coverage for physicians in-flight and would legislation similar to the on-the-ground Good Samaritan law protect physicians who are passengers on a plane?