Air Traffic Control Research Articles

Performance and energy-consumption evaluation of fuel-cell hybrid heavy-duty truck based on energy flow and thermal-management characteristics experiment under different driving conditions

In this study, energy-flow experiments are performed on a fuel-cell (FC) hybrid heavy-duty truck under constant vehicle speeds of 25 and 75 km/h at normal temperature (23 °C) and high temperature (35 °C), respectively. The vehicle energy consumption and distribution, the efficiency of the main systems and components, and the characteristics of the thermal-management system are analyzed comprehensively to evaluate the vehicle performance. Experiment results show that the energy-conversion efficiency of the FC stack is relatively low (45.6% to 50.25%) and that the output-power fluctuation of the FC system is relatively large, which indicate that the FC stack’s control of hydrogen and air may be unreasonable. Moreover, the battery-charging energy occupies a considerable proportion of the FC stack output energy, which results in the high internal resistance of the power battery under low-speed driving conditions. This implies that the vehicle’s power-matching and control strategies may not be reasonable. For the thermal-management system, the temperature difference between the inlet and outlet coolant of the FC coolant exceeds 10 °C in most cases, thus resulting in high thermal-energy losses. Additionally, the maximum temperature of the power battery core does not decrease significantly once the battery cooling cycle began. This indicates that the tested FC hybrid truck may be inappropriate in terms of structural design, accessory matching, and control of the thermal-management system. Furthermore, the highest difference in temperature between the head of the co-pilot and sleeper exceeds 10 °C in most cases, which results in an uncomfortable crew-cabin environment. This implies that the structure of the air duct and the number and position of the crew-cabin air outlets are inappropriate. In general, the efficiencies of the vehicle direct current (DC)/DC, motor, and transmission system are relatively low, particularly at low vehicle speeds (less than 90%). Existing problems associated with the entire vehicle and key components are identified, and the corresponding improvement suggestions are proposed based on experimental results pertaining to the energy-flow and thermal-management characteristics. Thus, this study provides an important reference and basis for the performance and energy-consumption optimizations of FC hybrid heavy-duty trucks in the future. Additionally, this study provides an effective method for evaluating the performance and energy consumption of FC hybrid vehicles.

Flight-Based Control Allocation: Towards Human–Autonomy Teaming in Air Traffic Control

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.

Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control

The booming air transportation industry inevitably burdens air traffic controllers’ workload, causing unexpected human factor-related incidents. Current air traffic control systems fail to consider spoken instructions for traffic prediction, bringing significant challenges in detecting human errors during real-time traffic operations. Here, we present an automation paradigm integrating controlling intent into the information processing loop through the spoken instruction-aware flight trajectory prediction framework. A 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the trajectory and spoken instructions, as well as minimize the data requirements. Experiments on a real-world dataset show the proposed framework achieves flight trajectory prediction with high predictability and timeliness, obtaining over 20% relative reduction in mean deviation error. Moreover, the generalizability of the proposed framework is also confirmed by various model architectures. The proposed framework can formulate full-automated information processing in real-world air traffic applications, supporting human error detection and enhancing aviation safety.

Design of the electrode structure for the dust removal efficiency optimization of electrostatic precipitator

Abstract Air dust pollution prevention and control is a global environmental concern. Electrostatic precipitators (ESPs) play a crucial role in dust pollution control, and its electrode structure significantly affects plasma parameters and the movement of charged dust particles, which in turn influences the dust removal efficiency. This study focuses on optimizing the commonly used wire-plate dust removal device, experimental research is conducted first, revealing that the hole-hole electrode structure of an ESP exhibits higher peak currents at the same voltage compared to both plate-hole and plate-plate electrode configurations. For the removal efficiency of particles with a radius greater than 1 μm, the ESP with a hole-hole electrode structure performs better than those with plate-hole and plate-plate electrode structures. Moreover, the particle collection efficiency is positively correlated with particle radius, larger particles correspond to higher dust removal efficiency. Based on experimental findings, simulations are conducted to provide a deeper analysis and understanding of the dust removal mechanisms of ESPs. This paper primarily utilizes COMSOL Multiphysics numerical simulation software to simulate traditional wire-plate ESPs and a new wire-hole ESP. It explores the multiphysical characteristics of ESPs under different electrode structure configurations, and investigates in depth the impact of electrode structure on dust removal efficiency. The simulation results indicate that compared to flat collecting electrodes, porous collecting electrodes exhibit a significant increase in electric field intensity at the openings, and they have a lower surface charge density than flat plates, thereby reducing reverse corona phenomena. Ion wind affects the internal airflow dynamics of ESPs, thereby influencing their efficiency in capturing fine particulate matter. This negative impact of ion wind can be mitigated by introducing perforations in the collection plates.

The HIAL storm: Resisting the technological transformation of air traffic control

AbstractWhether and under what conditions the implementation of technology can be resisted hinges on how labour can mobilise structural, associational, institutional, coalitional and ideational power resources. The failed attempt of Highlands and Islands Airports Limited (HIAL) to create a remote tower centre at Inverness to control air traffic at seven regional airports, with the loss of well‐paid jobs in local communities, demonstrates the importance of the composition and interaction of different power resources. By analysing a group of workers with abundant power resources during a ‘storm’ as well as a period of calm, we demonstrate how different power resources can complement but also compromise workers' interests, thereby explaining why unions might be hesitant to utilise specific resources over time and place.

Hybrid compressed air energy storage system and control strategy for a partially floating photovoltaic plant

Hybrid compressed air energy storage system and control strategy for a partially floating photovoltaic plant

Examining Job Satisfaction and Its Sub-Dimensions in Air Traffic Control

Examining Job Satisfaction and Its Sub-Dimensions in Air Traffic Control

The Use of Heart Rate Measures to Evaluate Stress Levels among Air Traffic Controllers

This paper looks at the possibility of using a heart rate monitor to evaluate stress levels among Air Traffic Controllers (ATCOs). Air Traffic Control (ATC) service requires constant good planning, situation awareness, and always making sound decisions. A study was conducted on two ATCOs for three complete work cycles: the morning shift from 8 a.m. to 4 p.m., the afternoon shift from 4 p.m. to 12 a.m., and the night shift from 12 a.m. to 8 a.m. Both participants have different roles, with Subject A being a planner controller while Subject B is a radar controller. The study used the NASA Task Load Index (NASA-TLX) survey to collect the subjective workload rating and a heart rate monitor armband to measure the heart rate reading of an ATCO and then compare it with the number of aircraft within the controlled area. Two hypotheses were investigated: the first one is the relationship between the number of traffic and heart rate readings, and the second hypothesis is the relationship between the elapsed working time and heart rate reading. The Spearman correlation test has been used to determine these relationships. Based on the results, a p-value of less than 0.05 and a positive correlation indicates a significant relationship between the number of aircraft and heart rate readings for both subjects. Elapsed time and heart rate measurement, on the other hand, do not significantly correlate with a p-value greater than 0.05 and a negative correlation was produced for both subjects. The unweighted NASA-TLX workload measure confirmed the findings for Subject A but not for Subject B. This might be due to the different roles carried out by both subjects during the experiment. This shows that different work tasks resulted in different workload-induced factors. The results show a promising outcome of using a heart rate monitor to evaluate stress levels among ATCOs. However, more subjects were needed to enable the use of this concept for future purposes of measuring ATCO stress levels to manage workload.

Analysis of Airport Operation Control Center (AOCC) Unit Services at Adi Soemarmo Boyolali International Airport

Airport Operation Control Center (AOCC) is the front line in supervising and controlling all problems and is the control center of Adi Soemarmo Solo International Airport operations in serving Airport Service Users. AOCC involves all stakeholders at the Airport by integrating the systems owned by each stakeholder so that they can operate effectively and efficiently. This study aims to determine how the AOCC unit service system is at Adi Soemarmo Boyolali International Airport and what factors can cause problems and solutions to AOCC unit service problems. This study uses a descriptive qualitative approach. The data used are primary and secondary data. Primary data is obtained from observation and interviews, while secondary data comes from literature studies and photographs related to the problems studied. The data analysis techniques used are data reduction, data presentation and drawing conclusions. To test the validity of the data, triangulation techniques are used. The research time is in August - September 2023. The results of this study indicate that a good service system was found by the Airport Operation Control Center (AOCC) Unit to service users starting from coordination with all parties, namely Airlines and Ground Handling, Air Traffic Control (ATC), Apron Movement Control (AMC), Information Unit, Aviation Security, Airport Operation Landside And Terminal (AOLT), Traffic Administration Office and Central Flight Schedule Office (CFSO) and the Airport Mechanical Unit. The service system uses the ACDM (Airport Collaborative Decision Making) system, namely with collaboration between stakeholders to share information between related parties in real time and make joint decisions in a win-solution manner. Factors that can cause problems are related to coordination between one and another so that it can harm several parties. The solution to various problems is to implement ACDM (Airport Collaborative Decision Making), where related units in airport operations synergize with each other, receive and share information from each unit, make proactive and appropriate joint decisions, anticipate unexpected events and provide the best quality of service, cohesiveness and unity of vision and mission.

Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.

Effective Air Traffic Control Learning: New Approaches and Innovative Methods

This research discusses more effective Air Traffic Control (ATC) learning through new approaches and innovative methods. The new approaches described include project-based, problem-based, skill-based, and game-based approaches. Meanwhile, the innovative methods discussed include simulation, virtual reality (VR), and augmented reality (AR). Factors that influence the effectiveness of ATC learning are also discussed, including the ability of instructors, facilities, and student motivation. This research used qualitative methods by conducting interviews with 10 ATC personnel and 10 ATC instructors, and performing a Two Sample Mean Test on 96 ATC students in Indonesia. Based on the analysis, it was found that innovative methods and new approaches can improve the effectiveness of ATC learning compared to Conventional methods. However, further research is needed to deepen the use of technology in ATC learning.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Enhanced Fear Extinction Through Infralimbic Perineuronal Net Digestion: The Modulatory Role of Adolescent Alcohol Exposure.

Perineuronal nets (PNNs) are specialized components of the extracellular matrix that play a critical role in learning and memory. In a Pavlovian fear conditioning paradigm, degradation of PNNs affects the formation and storage of fear memories. This study examined the impact of adolescent intermittent ethanol (AIE) exposure by vapor inhalation on the expression of PNNs in the adult rat prelimbic (PrL) and infralimbic (IfL) subregions of the medial prefrontal cortex. Results indicated that following AIE, the total number of PNN positive cells in the PrL cortex increased in layer II/III but did not change in layer V. Conversely, in the IfL cortex, the number of PNN positive cells decreased in layer V, with no change in layer II/III. In addition, the intensity of PNN staining was significantly altered by AIE exposure, which narrowed the distribution of signal intensity, reducing the number of high and low intensity PNNs. Given these changes in PNNs, the next experiment assessed the effects of AIE and PNN digestion on extinction of a conditioned fear memory. In Air control rats, digestion of PNNs by bilateral infusion of Chondroitinase ABC (ChABC) into the IfL cortex enhanced fear extinction and reduced contextual fear renewal. In contrast, both fear extinction learning and contextual fear renewal remained unchanged following PNN digestion in AIE exposed rats. These results highlight the sensitivity of prefrontal PNNs to adolescent alcohol exposure and suggest that ChABC-induced plasticity is reduced in the IfL cortex following AIE exposure.

Validating Flow-Based Arrival Management for En Route Airspace: Human-In-The-Loop Simulation Experiment with ESCAPE Light Simulator

The advancement of Arrival MANager (AMAN) is crucial for addressing the increasing complexity and demand of modern airspace. This study evaluates the operational feasibility and effectiveness of an innovative AMAN designed for en route airspace, the so-called En Route AMAN. The En Route AMAN functions as a controller support system, facilitating the sharing of information between en route air traffic controllers (ATCos), approach controllers (current AMAN), and airport controllers (Departure Managers) in airports with multiple runways. The En Route AMAN aims to support upstream ATCos by sequencing and spacing of incoming streams via speed control and runway assignment, thereby enhancing overall air traffic efficiency. Human-In-The-Loop simulations involving rated ATCos are performed under scenarios that replicate real-world traffic and weather conditions. These simulations focus on upstream airspace to assess the impact of En Route AMAN on delay mitigation and ATCos’ performance. Unlike previous studies that solely relied on theoretical models and fast-time simulation for operational feasibility evaluation, this approach incorporates ATCos’ real-time decision-making, situational awareness, and task management, addressing critical operationalization challenges. The results demonstrated that the En Route AMAN could reduce the average flight duration by up to 25.6 s and decrease the total number of ATCo instructions by up to 20% during peak traffic volume. These findings support that the En Route AMAN is both operationally viable and effective in mitigating arrival delays, highlighting the importance of Human-In-The-Loop for practical validation.

Study on Improvement of Docking Mechanism for Docking–Undocking Drones in the Air and Docking Control Using an Onboard Camera

In this study, we developed a docking mechanism for drones that enables docking and undocking in the air for cargo transportation. This capability allows the drone configuration to be adapted depending on the cargo size and shape. We introduced a novel docking mechanism that incorporates magnetic adsorption and a newly engineered locking mechanism. We developed a drone detection system capable of estimating the relative position of a target drone by utilizing an onboard camera to identify the infrared LEDs on the target drone via image processing. Moreover, flight experiments, including detection, docking, and undocking, were performed using a mock drone to demonstrate the effectiveness of the developed system.

Enhanced Speech Processing for Air Traffic Control Using an Optimized Generative Adversarial Network

In order to improve the quality of voice calls during air traffic control, an improved SEGAN air traffic control speech enhancement algorithm is proposed. Aiming at the problem that the traditional speech enhancement algorithm based on generative adversarial network (SEGAN) is drowned under low signal-to-noise ratio conditions, a multi-stage, multi-mapping, multi-dimensional output generator and a multi-scale, multi-discriminator network model are proposed based on the SEGAN network model. First, the speech semantic features are extracted based on the deep neural network structure to complete the semantic segmentation of air traffic control speech. Secondly, multiple generators are set to further optimize the speech signal. Then, a downsampling module is added to the convolution layer to improve the model's utilization of speech information and reduce the loss of speech information. Finally, multi-scale, multiple discriminators are used to learn the distribution law and information of speech samples in multiple directions. The results show that under low signal-to-noise ratio conditions, the improved SEGAN model improves the short-term objective intelligibility (STOI) and the perceptual evaluation of speech quality (PESQ) by and respectively , which can quickly and effectively perform air traffic control speech enhancement and provide preparation for subsequent air traffic control speech recognition.

The Challenges in Learning ICAO Radiotelephony: Students' Perspective

This study investigates challenges faced by Batch 13 Air Traffic Control (ATC) students at Politeknik Penerbangan Surabaya in mastering standardized ICAO Radiotelephony skills. Through qualitative methods like interviews, observations, and document analysis, the research uncovers various hurdles, from linguistic complexities to practical issues. Students struggle with English proficiency, accent variations, and applying phraseologies in real-world scenarios. The study highlights the importance of continuous practice and peer collaboration in overcoming these obstacles, emphasizing interactive pedagogy's role in fostering professional skills. By building on existing research, the thesis offers insights for aviation education enhancement, advocating tailored approaches to accommodate diverse learning needs. It calls for an inclusive learning environment to facilitate effective mastery of ICAO Radiotelephony skills.

Air Route Design of Multi-Rotor UAVs for Urban Air Mobility

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Scheduling optimization of continuous climb and descent operations in busy terminal airspace

Based on the current terminal airspace structure, a method for scheduling aircraft arrival and departure that integrates trajectory optimization, conflict detection and multi-objective optimization is proposed to help implement continuous climb and descent operations in busy terminal airspace. First, based on the multi-stage optimal control theory, the Gaussian pseudo-spectral method is used to propose a vertical profile optimization method for continuous climb and descent operations, and the trajectory optimization of continuous climb and descent operations based on the cost index is realized. Secondly, according to the wake turbulence interval and release interval used by the runway, and the horizontal and vertical intervals of air operations, the Mahalanobis distance is used to establish an aircraft conflict detection model. Then, considering the demands of air traffic control, airlines, airports and other operating units, a multi-objective scheduling model and method for aircraft arrival and departure that can achieve the optimization results are proposed. Finally, two sets of arrival and departure data at Guangzhou Baiyun Airport during busy periods are selected, multiple interval parameters are set, alternative paths are introduced, and case analysis and comparative studies are carried out. The results show that during the busy period dominated by departures, the terminal airspace of Guangzhou Baiyun Airport can achieve continuous climb and descent operations during busy periods; during the busy period dominated by arrivals, two aircraft could not be dispatched. The introduction of alternative paths can reduce the number of aircraft that cannot be dispatched.