A Three-Stage Hybrid SEM-BN-ANN Approach for Analyzing Airport Service Quality

The novel coronavirus (COVID-19) outbreak has impacted the aviation industry worldwide. Several restrictions and regulations have been implemented to prevent the virus's spread and maintain airport operations. To recover the trustworthiness of air travelers in the new normality, improving airport service quality (ASQ) is necessary, ultimately increasing passenger satisfaction in airports. This research focuses on the relationship between passenger satisfaction and the ASQ dimensions of airports in Thailand. A three-stage analysis model was conducted by integrating structural equation modeling, Bayesian networks, and artificial neural networks to identify critical ASQ dimensions that highly impact overall satisfaction. The findings reveal that airport facilities, wayfinding, and security are three dominant dimensions influencing overall passenger satisfaction. This insight could help airport managers and operators recover passenger satisfaction, increase trustworthiness, and maintain the efficiency of the airports in not only this severe crisis but also in the new normality.

Benefits and Barriers of Digital Procurement: Lessons from an Airport Company

Implementing a well-integrated procurement system and applying uniform practices to achieve the strategic goals of any company is a complex phenomenon. Navigating the digital procurement systems in achieving supply-chain resilience remains a predicament. Framed within the technology acceptance model (TAM), which is a key model in understanding the predictors of human behaviour toward the potential acceptance or rejection of the technology. This study explored the benefits and barriers of digital procurement at Airports Company South Africa (ACSA). A qualitative approach in a form of a single holistic case study design was adopted. The sample involved 18 employees and individuals who were supply chain management (SCM), information technology (IT), and programme management office (PMO) professionals. Semi-structured interviews conducted focused on those with extensive experience on procurement, digital technologies, procurement automation or the implementation of transformation programmes. Digital procurement is a value-adding function at ACSA with the possibilities of providing cost reduction in the supply chain. However, the participants highlighted job losses, cyber security, lack of interoperability, lack of skills and system downtimes as obstacles affecting the adoption of digital procurement and as organizational barriers. The infusion of digital technologies into various aspects of organisational processes and outcomes remains a complex, dynamic, fluid, and volatile phenomenon. A framework highlighting critical focus areas when it comes to the adoption of digital procurement of digitalization is presented.

Ultraviolet sterilization information provision system of baggage carts and arriving baggage for airports

In this study, we develop a system to provide information on the sterilization of baggage carts and arriving passenger baggage to airport (Hereafter referred as arrival baggage) by using ultraviolet (UV) sterilization and information communication technology as border quarantine measures at airports. This system sterilizes arrival baggage and baggage carts by UV irradiation, and allows passengers to easily view the sterilization information recognized by radio frequency indentation technology. This is to provide safety and security not only to passengers, but also to staff, who may come into direct contact with the arrival baggage, of airport, airline, customs, and so on. In addition, the passengers can be provided with baggage tracking information, such as the current location and estimated delivering time of the baggage. This makes it possible to keep social distancing at baggage claims as an infection prevention. Furthermore, we verify the feasibility of the developed system and identify the issues to be addressed for its practical application through a demonstration of proof of concept at Central Japan International Airport. © 2022 International Association of Traffic and Safety Sciences

A novel design of a smart interactive guiding robot for busy airports

The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.

Significance of Face Mask detection

The new coronavirus, initially detected in Wuhan, China, has spread worldwide, wreaking devastation. The speed with which it spreads and the severity of the epidemic have made this a global emergency. COVID-19 is a very concerning disease from a public health perspective, making it critical to take precautions against its transmission, such as limiting close personal contact and using protective gear. The primary objective of this research is to create a system that can recognise human face masks and determine whether individuals are attempting to maintain social distance. Alterations to everyone's daily routines. In such stages, everyone must always keep their identity concealed. Because the massive number of populations has changed since the Outbreak of the Coronavirus pandemic, finding people who are not wearing veils is a challenge. The global spread of COVID-19 has altered society. Many of us are staying in our homes, avoiding contact with city dwellers, and adjusting our routines-such as when and where we go to work or school-in ways we never would have imagined. We need updated timetables as we transition away from outmoded procedures. What stands out the most is the widespread practise of hiding one's face behind a veil or other kind of covering whenever we enter a public building. Wearing a veil or covering one's face may provide some comfort while also preventing the spread of the COVID-19 virus. By preventing anybody, even the unwitting carriers, from spreading the virus, widespread usage of protective clothing has the potential to significantly reduce the rate of disease spread in a given area. Thus, the importance of the veil and its identification are made very plain. There has been a rise in the importance of face recognition frameworks, which are especially useful in hospitals and medical clinics where privacy of patients is a concern. They're also vital in places like airports, sports stadiums, warehouses, and other such crowded areas where heavy foot traffic necessitates strict security measures to ensure everyone's safety. The framework of face veil recognition can ensure our safety and the safety of those around us. This assignment may serve as a digitally administered test anywhere from a classroom to a hospital to a bank to an airport terminal. Through the use of photo processing and extensive learning, we are able to recognise human faces and separate them into two groups, those with and without head coverings. The assignment will let a person who is responsible for screening people to do so even if they are located at a remote location, while still being able to effectively screen and provide guidance. Open CV, Tensor Flow, and Keras are some of the Python libraries used. With Deep Learning, As part of their model preparation, these activities make use of Convolution Neural Networks, a subset of Deep Neural Networks. © 2022 IEEE.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

The Impact of COVID-19 Pandemic on Airport: An Empirical Study of Service Quality, Customer Satisfaction, and Travel Intention for Sustainable Airport Operations

Delivering a high service quality under the safety protocol of COVID-19 is very essential for the sustainable airport operations. The study was intended to determine the impact of COVID-19 on Airport Service Quality (ASQ), customer satisfaction, and travel intention by utilizing a structural equation modeling (SEM) approach. A total of 517 Filipinos voluntarily answered an online questionnaire that consists of 92 questions. SEM indicated that the security check, terminal facilities, and services had significant effects on perceived value which subsequently led to customer satisfaction. In addition, travel safety measures had direct effects on Filipinos' travel intention and customer satisfaction. Interestingly, service innovations had no significant impact on customer satisfaction but directly affected travel intention. By understanding the relationship between these factors, airport management could have better decision-making while efficiently and effectively utilizing the resources in these times of uncertainty. © 2022 IEEE.

Existing approaches in Ear biometrics

Biometric authentication is a self-sufficient technique to prove one's identity that could be used in various security authentication platforms such as airport immigration control, customer authentication, cyber forensics, and many others. Security and privacy are significant concerns in today's world. Using biometrics traits, we could achieve a superior level of security. The covid-19 virus almost fails the other biometric system. As we have become a mask-wearing society due to which face recognition system was failing, and we know the virus is spread through contact, the fingerprint biometric system also fails. Ear biometrics could have become a promising and helpful field to prove one identity over other biometrics. Various researches have been done with reasonable accuracy but in a constrained environment. Ear biometrics can also come over the significant hurdle of security concerns. A review of many existing techniques is conducted in this paper to determine which algorithm performs better and delivers higher accuracy. This paper contains findings from numerous ear detection studies and suggests a future-related method that will provide good efficient accuracy in ear detection under an unconstraint database. © 2022 IEEE.

Prospects for Increasing the Flight Network in the Ukrainian Aviation Market in the Context of the COVID-19 Pandemic in the Context of the Agreement on a Common Aviation Area

The article examines the prospects for increasing the flight network in the context of the COVID-19 pandemic in the context of the agreement on a Common Aviation Area (CAA). The authors stressed that Ukraine is one of the few countries in the world with a completed aviation development cycle and occupies a leading position in the global market of transport and regional passenger aviation. It is proved that COVID-19 has become a unique challenge and threat to the existence and functioning of one of the largest and most important industries, such as civil aviation. A brief analysis of the work of aviation enterprises in Ukraine testifies to the catastrophic economic situation that has developed at the airfields of our country under the influence of the Covid-19 pandemic. It is proved that the Common Aviation Area agreement allows the gradual introduction into the legislation of Ukraine of more than 60 EU standards and directives in the field of flight safety, aviation security and air traffic management. It was noted that an important factor in signing the Agreement on the establishment of CAA is the production and renewal of the airline fleet with modern aircraft, increasing the level of passenger service and further modernization of airports. With the entry into force of the CAA Agreement, Ukrainian airlines can easily participate in any European airport and compete in different directions with European airlines. The authors highlight the prospects for increasing the flight network in the context of the COVID-19 pandemic in the context of the agreement on a common aviation area. © 2022 Kaunas University of Technology. All rights reserved.

INNOVATIONS FOR SATISFIED PASSENGERS AT RIGA INTERNATIONAL AIRPORT

None of international airports were prepared for the impact of COVID-19 global pandemic. Fortunately, international airports, also Riga International Airport (hereinafter - RIX), were not caught entirely unprepared for the crisis. Aviation industry has been deeply invested in contactless technology for years. Innovation has been a pillar of strength and growth of aviation industry over the past years. The research problem lies in decreased passenger demand of RIX services due to COVID-19. It strengthens a necessity to establishment of innovative solutions and development of touchless airport which might increase passenger satisfaction and renew airport services. Technology, coupled with heightened focus on automation defines passenger experience at RIX airport. Safety and security of passengers and staff are top priorities for RIX airport. Innovations not only enhance operational efficiency and security, but they also make the airport experience quicker and comfortable for passengers. Although digitalization, automation and touchless airport solutions are shaping the future of RIX airport, human recourses still have a crucial role, particularly in terms of providing friendly service and ensuring passengers enjoying the experience. Methodologically, this study interprets results from a survey, expert interviews, knowledge transfer to empirically measure passenger satisfaction with statistical and observational data. In order to observe local and international airport expert opinions, semi-structured interviews are conducted and analysed with the qualitative data processing method NVivo 12. © 2022 Latvia University of Life Sciences and Technologies. All rights reserved.

Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.

Face Mask Detection: A Deep Learning Concept

Face Mask Detection program developed by OpenCV, Keras / Tensor Flow uses Deep Learning ideas and laptop Vision to detect face masks on continuous still images such as video streaming. Within the gift situation thanks to COVID-19, there are no requests for the acquisition of a mask to save the area that is currently in dire need of transport, crowded areas, accommodations, major manufacturers and various businesses to guarantee the safety. Also, lack of big image data with a mask has made this task even more difficult and difficult. Following the outbreak of the global COVID-19 epidemic, there was an urgent need for preventive measures, with a mask on the face. The primary purpose of the project is to determine whether there is a face mask on people's faces in video and photos streamed live. We used the depth of learning to build our face detection model. The features used for object detection are the Single shot detector (SSD) due to its performance with precision and high speed. Apart from this, we used basic concepts to transfer learning to neural networks without ultimately excluding the presence or absence of facial image in a photo or video stream. Test results show that our model works at 100% efficiency with 99% accuracy of test and memory, respectively. Our mask detector did not use any data from the inserted images. The model is accurate, and as we are accustomed to using the design of MobileNetV2, savings are calculated collaboratively and therefore make it a lot easier to move the model to embedded programs like Google Coral & Raspberry Pi etc. This program will be used for time programs that require the acquisition of face masks for security operations due to the emergence of COVID-19. The project is used with the installed plans for use at airports, train stations, offices, school premises and marketplaces to ensure that the local unit of community safety tips is followed. © 2022 IEEE.

The Intelligent System for Mask Detection using Deep Learning

The main goal of the paper is to develop and design an intelligent system for automatic conditional access in critical virologic situations. The detection of proper use of face-mask, touchless temperature measurement, and counting incoming/outcoming people for single or multiple entrance doors or gates are the main objectives of this system. Originality and innovativeness of the paper are already in the idea, to create a new generation of affordable guard systems based on artificial intelligence with an emphasis on the pandemic situation in the world. In this paper, we will describe relevant works that used deep learning in security with compliance with pandemic regulations in comparison with proposed solution. The proposed technology will enable the start of a new generation of guard systems based on artificial intelligence with an emphasis on the pandemic regulations. This will create space for innovative solutions in the security of buildings, shops, factories, public transport stations, airports, etc. The implementation of this technology can bring revolutionary changes in society in actual situation and in the future. © 2022 IEEE.

Benchmarking Crowd-Counting Techniques across Image Characteristics

Crowd-counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd-counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd-counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd-counting methods can provide accurate and usable results. © 2021 Electrotechnical Society of Slovenia. All rights reserved.

After the Epidemic, Is the Smart Traffic Management System a Key Factor in Creating a Green Leisure and Tourism Environment in the Move towards Sustainable Urban Development?

The purpose of this study is to explore, after the epidemic, the intelligent traffic management system, which is the key to creating a green leisure tourism environment in the move towards sustainable urban development. First, quantitative research, snowballing, and convenience sampling methods are used to analyze 750 questionnaires with a basic statistical test, t-test, ANOVA test, and the Pearson product–moment correlation coefficient (PPMCC) method. Qualitative research and a semi-structured interview method are used to collect the opinions of six experts on the data results. Finally, the results are discussed with the multivariate inspection method. Although the current electric bicycle system is convenient, the study found that the service quality of the airport is sufficient;that the fare of the subway is low and popular with students if the system can ease the crowd during peak hours;and that the login and security check time can be shortened, which can help improve the operating convenience of the system interface and link the information of leisure and tourism activities. On the other hand, adjusting fares, increasing seats, planning for women-only ticketing measures and travel space, providing disinfection or cleaning facilities in public areas, and improving passenger’s public health literacy and epidemic prevention cooperation will further enhance the student travel experience, improve the smart city and green tourism network, and help achieve sustainable urban tourism.

Emotional analysis of safeness and risk perception of London and Rome airports during the COVID-19 pandemic

The evaluation of perceived safeness and risk by individuals is really useful for security and safety managing. Every individual is founded on the opinion of other individuals to get a selection and the Internet personifies the location where these judgments are mainly sought, obtained, and evaluated. From this point of view, social networks are characterized by a significant effect. Due to this reason, Opinion Mining and Sentiment Analysis have found remarkable uses in various environments and one of the most interesting is embodied by public security and safety. The aim of this work is to study the perception of risk of aircraft passengers and users of airports of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the airports of London Heathrow and Gatwick and the airports of Rome Fiumicino and Ciampino were studied, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents existing in Twitter. © 2021 IEEE.

IoT Enabled Smart Aiport System to Avoid Covid-19

Today's generation wants everything easier, faster and automatic. During this corona pandemic, health and safety of each and every individual, either who is traveling through flights or working at an airport is a big issue. Usually, when we go to an airport, we go through many checks, and before boarding the flight, the security check-in, our luggage bags are counted and tagged by the person working at the counter of the airport. The luggage bags are put on the conveyor belt and the person working at the counter has to count the luggage bags by himself, he has to stick the tags on the luggage bags. None of the airports provides the facility of automatic counting of the luggage bags and sticking tags on them. And during this COVID-19 pandemic, we should avoid touching maximum things. This research paper provides a new technique for the same and that in a smart way. In this research, we are providing a novel approach to create an automatic system which will help to make the airport a smart one with IOT sensors and devices. Smart Airport also provides the counting of the luggage bags, tagging of the luggage bags, checking the presence of metallic objects in the luggage bags in a single embedded system. This approach will help the human society in maintaining social distancing and help them to save their time. © 2021 IEEE.

Covid-19-Related Challenges for New Normality in Airport Terminal Operations

Airport operations are undergoing significant change, having to meet pandemic requirements in addition to intrinsic security requirements. Although air traffic has declined massively, airports are still the critical hubs of the air transport network. The new restrictions due to the COVID-19 pandemic pose new challenges for airport operators in redesigning airport terminals and managing passenger flows. To evaluate the impact of COVID-19 restrictions, we implement a reference airport environment. In this reference Airport in the Lab environment we will demonstrate the operational consequences derived from the new operational requirements. In addition, countermeasures to mitigate any negative impacts of these changes are tested. The results highlight emerging issues that the airport will most likely face and possible solutions. Finally, we could apply the findings and lessons learned from our testing at our reference airport to a real airport. © 2021 IEEE.

Face Mask Detection using CNN

Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person’s face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security. © 2021 IEEE.