Analysis of Motor Vehicle Accidents: Comparison Between Before and During the COVID-19 Lockdown in Maharashtra, India.

To prevent the pandemic spread of human-to-human transmitted diseases such as COVID-19, governments commonly resort to countrywide or regional lockdown strategies. Such lockdowns, whenever and wherever implemented, curtail the movement of persons and vehicles, and drastically alter traffic conditions. This study focuses on the effect of drastic and sudden changes in the traffic conditions, during the COVID-19 lockdown in the State of Maharashtra in India, in March-June 2020, on the numbers of motor vehicle accidents (MVAs), and the resultant fatalities and injuries. Content analysis of police-reported first information reports (FIRs) of MVAs is performed, and these lockdown trends are compared with archival data from corresponding previous (normal) periods. The statistical analysis shows that, during the lockdown, while the total number of MVAs fall drastically, they are more severe and have a much higher fatality rate per MVA. Also, the pattern of vehicles involved in MVAs, and resultant pattern of fatalities, also changes during lockdowns. The paper explores the reasons for these changed patterns and provides suggestions to reduce these negative externalities of pandemic related lockdowns.

Green Commercial Aviation Supply Chain—A European Path to Environmental Sustainability

The last century has witnessed European commercial aviation flourishing at the cost of environmental degradation by boosting greenhouse gas and CO2 emissions in the atmosphere. However, the outcry for net-zero emissions compels the sector's supply chain to a minimum 55% reduction of greenhouse gas emissions below the 1990 level by 2030 and zero CO2 emissions by 2050. This study examines a European environmental sustainability path toward a green commercial aviation supply chain. Driven by literature and a review of related documents, two propositions were advanced to orient perspectives on the relationship between pollution and the commercial aviation supply chain and actions being taken toward environmental sustainability. In semi-structured interviews, seventeen aerospace associates endorsed pollution sources in the commercial aviation supply chain during the four stages of the aircraft life cycle, including extracting the raw materials, manufacturing, ground and flight operations, and end-of-service. They recommended transitioning into green commercial aviation through the widespread deployment of innovative technologies, from modifying airframes to changing aviation fuel, utilizing alternative propulsion systems, adopting circular manufacturing, and improving air traffic management.

Tennessee Section of ITE-Meaningful Meetings, Meet-Ups, and More!

The Tennessee Section of ITE (TSITE) has strong local Section meetings, which were allowed and encouraged in 2022 as conditions improved from the COVID-19 pandemic. In-person meetings resumed in each of the larger cities: Memphis, Nashville, Chattanooga, Knoxville, and Johnson City. Technical sessions at meetings included applications of cutting-edge transportation elements and offered professional development hours (PDHs) for participants. TSITE was able to host all four of its quarterly Section meetings in person. The quarterly meetings rotate across the state and are typically full-day events. They begin in the morning with technical presentations and lunch is provided, followed by the business meeting, additional technical sessions, and/or a technical tour. In 2022, the Summer and Fall Section meetings were 3-day events. In 2022, the quarterly meetings were hosted and well-attended. The Winter Meeting in Cookeville TN at Tennessee Tech University in February had 71 participants. Meeting presentations included "MDOT Traffic Signal Asset Management & Preventative Maintenance," "Campus Like Emergence Evaluation Modeling," "Evaluating factors associated with Abandoned and Disabled Vehicle Incidents in Tennessee," and "MAQ award for deploying an NDOT Traffic Management Center and other emerging projects."

Viewing the changes in community annoyance due to aircraft noise over the times

More than 60 years have passed since the introduction of jet aircraft to civil aviation, and technological innovations have made aircraft much quieter. Nevertheless, people still complain that they experience severe suffering from aircraft noise. The changes in lifestyles, values concerning the sound environment, and aircraft operating conditions including the air traffic control system, over time, may have influenced the differences in annoyance responses. This paper overviews and considers the changes over time in the aircraft sound exposure level around the airport and the community annoyance caused by aircraft noise. Then it discusses the issue of recent noise complaints associated with the introduction of new air traffic management systems and flight routes as well as views the impact of coronavirus pandemic over the last two years or longer. Finally, it gives a minor consideration to how we should deal with these changes in the annoyance response. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Deep Learning Architecture for UAV Traffic-Density Prediction

The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average. © 2023 by the authors.

Structural analysis of air passenger flow in the Republic of Croatia from the COVID–19 pandemic impact perspective

Air transport is a globally significant mode of transport that carries people and goods over relatively long distances in a relatively short time. In the case of the Republic of Croatia there are some relevant geographical factors that make it necessary to connect the country to domestic and international airlines, such as territorial division (Dubrovnik's disconnection from the rest of the Croatian mainland), rugged shape and mountainous terrain. In addition, as a tourist destination, the country should offer good transport links to provide the best experience for tourists. The aim of this research is to conduct an in-depth structural analysis of the intensity and dynamics of passenger air traffic at the global, European, and national (i.e. Croatian) level, considering the specific crisis of the COVID-19 pandemic as a relevant factor in the development of passenger flow. To achieve the defined objective of this research, a structural analysis of the relevant indicators for the development of passenger flow at the airports of the Republic of Croatia was conducted. In addition, a comparative analysis of the intensity, structure and dynamics of passenger flow was carried out within the framework of a comparison of the relevant criteria and statistical data on the indicators of passenger flow at the international, regional, and national levels. The COVID-19 pandemic has significantly affected the state of passenger flow in air transport in terms of organisation and logistics, and especially in terms of the intensity and dynamics of passenger flows. Accordingly, uniform criteria for monitoring passenger flow, systematisation and structural analysis of relevant formation indicators are the basic requirements for continuous monitoring and planning of passenger flow in air transport. © Faculty of Maritime Studies Rijeka, 2022.

Metro Network Operational Solutions for Connectivity Control based on Percolation Theory

Recent studies have applied the percolation theory to analyze the connectivity of networks in the transportation field. However, research was conducted in a manner that completely removed the function of nodes or links. There was a limit in that applying public transportation was difficult to guarantee the right to move the captive rider. In this study, penalties were imposed on public transportation nodes in the form of wait times to remove the function of node partially. Accordingly, the travel time of a network was calculated by optimal strategy assignment to reflect passenger behavior. When nodes were randomly penalized without transfer distinctions, there was a critical point of travel-time increase between cases with penalties of 50 and 60 nodes, respectively, and percolation was observed indirectly. A large and global effect of increased travel time was observed when the penalties were issued only to transfer stations. The application of a trip frequency weight increases the effect of penalties on medium- or short-timed trips. The results of this study can be used to establish quarantine policies for controlling public transportation networks. Furthermore, it is the first attempt at observing percolation by partially limiting its function in the form of node penalties in a public transportation network.

An Analysis of Flight Routes and Considerations for Free Route Airspace Implementation in Fukuoka FIR

The Electronic Navigation Research Institute and Korea Aerospace University have proposed an initial Free Route Airspace (FRA) concept for the Fukuoka and Incheon Flight Information Regions (FIR) to improve air traffic flows and air traffic management in northeast Asia. We are now working to elaborate the concept, quantify benefits, and identify implementation issues. This paper examines two air traffic flows in Fukuoka FIR: (1) Japanese domestic flights between the highest traffic city pairs, and (2) overflight traffic between Korea and North America across radar-controlled airspace. From an analysis of operations based on flight plan and radar data for 2019, prior to the COVID-19 pandemic, FRA design and implementation issues are considered. Our analysis and findings are expected to contribute to the planning of FRA implementation in Northeast Asia.

Towards AI-based Air Traffic Control

Air traffic, despite the recent dip due to Covid, is expected to grow 30-40% year on year. With the potential inclusion of UAVs (Unmanned Aerial Vehicles) into controlled airspace over the next decade, it is anticipated that the congestion levels in airspace will increase 10 fold. This paper presents an AI-based approach to air traffic control, with the aim of alleviating the load and improving the efficiency of human agents (air traffic controllers). One of the primary goals of air traffic control is to safely navigate an aircraft through controlled airspace using real-time control actions - such as changes to speed, heading (direction of travel) and altitude of an aircraft. The safety critical nature of this environment calls for precise explanations (why take an action) and counterfactual (why not take an action) explanations, real-time responsiveness, the ability to present succinct actions to a human agent, while simultaneously optimizing for air traffic delays, fuel burn rates, and weather conditions. This paper presents algorithms and a system architecture for anticipating separation losses (conflicts in airspace) and a lattice-based search space exploration AI planner to recommend actions to avoid such conflicts. The key contributions of the paper include: (i) fast detection (prediction) of conflicts in a controlled airspace, and (ii) fast lattice space exploration based AI solver to produce a set of feasible resolutions for the detected conflicts. Additionally, this paper discusses how to weight the different resolutions and how future work on optimisation techniques could improve the efficiency of the algorithm and address various known limitations of the current approach from both technical and human-agent perspective. The evaluations are conducted against an air traffic simulator, Narsim, showing the ability to avoid separation losses, while minimizing the number of actions even at 3 x normal capacity. © ATM 2021. All rights reserved.

On-line platform for the short-term prediction of risk of expansion of epidemics: Proof-of-concept based on COVID-19 evolution

This paper proposes a novel approach for the prediction of the risk of expansion of local epidemics to 3rd regions or countries in the world through the air traffic network. The approach relies on the definition of a new indicator, the Imported Risk, which represents the overall risk of having infected individuals entering an airport from any other airport with connections. We performed a proof-of-concept of the proposed approach by using daily data of the air traffic movements on a global scale and of the evolution of the COVID-19 epidemic at the beginning of 2020. For that purpose, we developed a complex network model based on Tagged Graphs to calculate the Imported Risk indicator, together with other complementary indicators showing the centrality of the air traffic network weighted with the Imported Risk. We implemented our complex network model into an on-line platform which provides the daily risk of expansion of the epidemic to other regions or countries. The platform supports the identification of the components of the network (airports, routes…) that have a major impact on the risk of expansion. The paper also provides findings on how the short-term prediction of diseases' expansion through the Imported Risk indicator allows the identification of effective measures to take control of the virus spread. © ATM 2021. All rights reserved.

Quantifying the Impact of Air Travel on Growth of COVID-19 Pandemic in the United States

This paper develops models to quantify the dynamics of the impact of air travel on the spread of the COVID-19 pandemic, using a wide range of datasets covering the period from March to December 2020. With the help of flight operation data, we first develop a novel approach to estimate the county-level daily air passenger traffic, which combines passenger load factor estimates and information about the air traffic distribution. Cross-sectional models using aggregated county-level variables are estimated. While this study focuses on air travel variables, we also control for potential spatial autocorrelation and other relevant covariates, including vehicle miles traveled (VMT), road network connectivity, demographic characteristics, and climate. The model results indicate that air travel has a strong and positive impact on the initial pandemic growth rate for both case-based and fatality-based aggregate models. © ATM 2021. All rights reserved.

Wings India 2022

BEL and AAI collaborate on Air Traffic Management Systems In a major boost to its own diversification drive into non-defence and the Government's 'Make in India' programme, Bharat Electronics Limited (BEL) and Airports Authority of India (AAI), under it's R&D initiative, at Wings India 2022, entered into an agreement for the joint, indigenous development of systems for air traffic management and surface movement of aircraft at airports in the country which were hitherto being imported. Under this Agreement, BEL and AAI will jointly develop Civil Air Traffic Management System (ATMS) with Advanced-Surface Movement Guidance and Control System (ASMGCS), a complex ground surveillance system that manages air traffic at airports and in Indian Civil Airspace for safe operation of flights from take-off to landing. The aim of ATMS with ASMGCS is to provide the air traffic controller with the complete air traffic picture of the coverage area while interacting with Primary/ Secondary Radar, Automatic Dependent Surveillance-Broadcast (ADS-B), Multi-lateration System (MLATs), and navigational equipment such as GPS, Instrument Landing System (ILS) and Doppler Very High Frequency Omni Range (DVOR). Boeing: India to lead South Asia air traffic growth Boeing shared projections for South Asia's commercial aviation sector over the next 20 years, with the region leading the world in yearly passenger traffic growth.

Optimization of Aircraft Taxiing Strategies to Reduce the Impacts of Landing and Take-Off Cycle at Airports

The increasing attention of opinion towards climate change has prompted public authorities to provide plans for the containment of emissions to reduce the environmental impact of human activities. The transport sector is one of the main ones responsible for greenhouse emissions and is under investigation to counter its burdens. Therefore, it is essential to identify a strategy that allows for reducing the environmental impact produced by aircraft on the landing and take-off cycle and its operating costs. In this study, four different taxiing strategies are implemented in an existing Italian airport. The results show advantageous scenarios through single-engine taxiing, reduced taxi time through improved surface traffic management, and onboard systems. On the other hand, operating towing solutions with internal combustion cause excessive production of pollutants, especially HC, CO, NOX, and particulate matter. Finally, towing with an electrically powered external vehicle provides good results for pollutants and the maximum reduction in fuel consumption, but it implies externalities on taxiing time. Compared to the current conditions, the best solutions ensure significant reductions in pollutants throughout the landing and take-off cycle (−3.2% for NOx and −44.2% for HC) and economic savings (−13.4% of fuel consumption).

A Comparison of Approaches for the Time Series Forecasting of Motorway Traffic Flow Rate at Hourly and Daily Aggregation Levels

Congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. Congestion is generally associated with reduced average speed at a high traffic flow rate. This traffic flow rate is defined as the number of vehicles that pass a certain location at a given time. The modelling and prediction of this traffic flow rate may lead to valuable insights that may be used to reduce congestion and societal costs. This study aims to predict the traffic flow rate for 41 different locations in and around Amsterdam, The Netherlands. Using TBATS, SARIMAX and LSTM models, among others, the traffic flow rate of these locations has successfully been modelled. These models may provide accurate predictions for the future flow rate, which may be useful for the identification of infrastructure bottlenecks and the scheduling of maintenance. Considering the dramatic effects of the COVID-19 pandemic on the traffic flow rate, the inclusion of 2020 data with a number of external factors, could lead to an improvement of the results and the ability to model the future effects of the pandemic. © 2022 Elsevier B.V.. All rights reserved.

From the Editors of the Special Issue on Current Applications and Innovations of Artificial Intelligence and Machine Learning in Aerospace

The articles in this special section focus on current applications and innovations of artificial intelligence and machine learning in aerospace. Artificial intelligence (AI) and machine learning (ML) play an increasingly important role in aerospace applications and serve various military, commercial aviation, and space exploration sectors to ensure safety, dependability, and customer loyalty. AI/ML contributes to provide various automated systems used in aviation, such as fuel efficiency, smart maintenance, smart air traffic management, pilot training, passenger identification, threat identification, remote sensing, and fully autonomous aerial vehicles among other systems. AI/ML is concerned with algorithms and techniques that allow systems to “learn” and “reason” based on algorithms and techniques employing computational and statistical methods. It can significantly enhance speed, efficiency, workload, and safety to enable the integrating of more complex technologies, such as autonomous visionbased navigation and data ecosystems. Recently advanced data analytics provided the aviation industry a way to respond to COVID and advise airlines on when to swap aircraft for bigger or smaller planes and how the global health restrictions may change flight schedules. While there are many other innovative use cases of AI/ML in aviation and aerospace, the overarching conclusion is that the implementation must be driven by safety.

Harmonization and Verification of Three National European Icing Forecast Models Using Pilot Reports

The Single European Sky Air Traffic Management Research (SESAR) program aims at modernizing and harmonizing the European airspace, which currently has a strongly fragmented character. Besides turbulence and convection, in-flight icing is part of SESAR and can be seen as one of the most important meteorological phenomena, which may lead to hazardous flight conditions for aircraft. In this study, several methods with varying complexities are analyzed for combining three individual in-flight icing forecasts based on numerical weather prediction models from Deutscher Wetterdienst, Météo-France, and Met Office. The optimal method will then be used to operate one single harmonized in-flight icing forecast over Europe. As verification data, pilot reports (PIREPs) are used, which provide information about hazardous weather and are currently the only direct regular measure of in-flight icing events available. In order to assess the individual icing forecasts and the resulting combinations, the probability of detection skill score is calculated based on multicategory contingency tables for the forecast icing intensities. The scores are merged into a single skill score to give an overview of the quality of the icing forecast and enable comparison of the different model combination approaches. The concluding results show that the most complex combination approach, which uses iteratively optimized weighting factors for each model, provides the best forecast quality according to the PIREPs. The combination of the three icing forecasts results in a harmonized icing forecast that exceeds the skill of each individual icing forecast, thus providing an improvement to in-flight icing forecasts over Europe.

LDACS Broadcast Digital Voice Concept and Expected Performance

The amount of air traffic is rapidly recovering from the COVID-19 pandemic and beginning to rise above previous levels. As a result, the VHF band is expected to become saturated in the near future, harmfully affecting air traffic management. As one solution for the increased need for aeronautical connectivity, the terrestrial LDACS data link has been designed and is in the process of ICAO standardization. However, LDACS development has been primarily focused on data communication and digital voice protocols have not been fully defined yet. This paper presents the proposed LDACS digital voice architecture developed in the framework of the Single European Sky ATM Research (SESAR) program. The LDACS digital voice air-ground communication protocol is validated and evaluated in computer simulations. It is shown that the SESAR-specified functional and performance requirements are satisfied. © 2022 IEEE.

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.

The omnidirectional runway with infinite heading as a futuristic runway concept for future free route airspace operations

Purpose>The purpose of this paper is to create and analyze the effectiveness of a new runway system, which is totally created for the future free route operations.Design/methodology/approach>This paper researches and analyses the new generated runway concept with the fast time simulation method. Fuel consumption and environmental effect of the new runway system are calculated based on simulation results.Findings>According to different traffic density analyses the Omnidirectional Runway with Infinite Heading (ORIH) reduced fuel consumption and CO2 emissions up to 46.97%. Also the total emissions of the ORIH concept, for the hydro carbon (HC), carbon monoxide (CO) and nitrogen oxides (NOx) pollutants were lower than the total emissions with the conventional runway up to 83.13, 74.36 and 51.49%, respectively.Practical implications>Free route airspaces bring many advantages to air traffic management and airline operations. Direct routes become available from airport to airport thanks to free route airspace concept. However, conventional single runway structure does not allow aircraft operations for every direction. The landing and take-off operations of a conventional airport with a single runway must be executed with only two heading direction. This limitation brings a bottleneck direct approach and departure route usage as convenient with free route airspace concept. This paper suggests and analyzes the omnidirectional runway with infinite heading (ORIH) as a solution for free route airspace.Originality/value>This paper suggests a new and futuristic runway design and operation for the free route operations. This paper has its originality from the suggested and newly created runway system.

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.