Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Air Traffic Volume and Air Traffic Control Human Errors

Navigable airspaces are becoming more crowded with increasing air traffic, and the number of accidents caused by human errors is increasing. The main objective of this paper is to evaluate the relationship between air traffic volume and human error in air traffic control (ATC). First, the paper identifies categories and elements of ATC human error through a review of existing literature, and a study through interviews and surveys of ATC safety experts. And then the paper presents the results of an experiment conducted on 52 air traffic controllers sampled from the Korean ATC organization to find out if there is any relationship between traffic volume and air traffic controller human errors. An analysis of the experiment clearly showed that several types of ATC human error are influenced by traffic volume. We hope that the paper will make its contribution to aviation safety by providing a realistic basis for securing proper manpower and facility in accordance with the level of air traffic volume.

En-route Sector Complexity Control Strategies in Air Traffic Management

Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.

Empirical Exploration of Air Traffic Control Behaviour at Terminal Maneuvering Area:From an Air Traffic Flow Aspect

In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway refers to the time interval between successive flying aircraft in air traffic flow,which is one of the most important characteristics of air traffic flow.The variation in aircraft headway reveals the air traffic control behaviour.In this paper,we study the characteristics of air traffic control behaviours by analyzing radar tracks in a terminal maneuvering area.The headway in arrival traffic flow is measured after the determination of aircraft trailing relationships.The headway evolutionary characteristics for different control decisions and the headway evolutionary characteristics in different phase-states are discussed,and some interesting findings are gotten.This work may be helpful for scholars and managers in understanding the intrinsic nature of air traffic flow and in the development of intelligent assistant decision systems for air traffic management.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Integrated Evaluation of Air Traffic Controller Workload Based on Matter-Element Analysis

A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.

Effect of Working Experience on Air Traffic Controller Eye Movement

Eye movement is an important indicator of information-seeking behavior and provides insight into cognitive strategies which are vital for decision-making.Various measures based on eye movements have been proposed to capture humans’ability to process information in a complex environment.The effectiveness of these measures has not yet been fully explored in the field of air traffic management.This paper presents a comparative study on eye-movement measures in air traffic controllers with different levels of working experience.Two commonly investigated oculomotor behaviors,fixation and saccades,together with gaze entropy,are examined.By comparing the statistical properties of the relevant metrics,it is shown that working experience has a notable effect on eye-movement patterns.Both fixation and saccades differ between qualified and novice controllers,with the former type of controller employing more efficient searching strategies.These findings are useful in enhancing the quality of controller training and contributing to an understanding of the information-seeking mechanisms humans use when executing complex tasks.

Identification of Information-Seeking Behaviors from Air Traffic Controllers′ Eye Movements

Air traffic controllers are the important parts of air traffic management system who are responsible for the safety and efficiency of the system.They make traffic management decisions based on information acquired from various sources.The understanding of their information seeking behaviors is still limited.We aim to identify controllers′ behavior through the examination of the correlations between controllers′eye movements and air traffic.Sixteen air traffic controllers were invited to participate real-time simulation experiments,during which the data of their eye ball movements and air traffic were recorded.Tweny-three air traffic complexity metrics and six eye movements metrics were calculated to examine their relationships.Two correlational methods,Pearson′s correlation and Spearman′s correlation,were tested between every eye-traffic pair of metrics.The results indicate that controllers′two kinds of information-seeking behaviors can be identified from their eye movements:Targets tracking,and confliction recognition.The study on controllers′ eye movements may contribute to the understanding of information-seeking mechanisms leading to the development of more intelligent automations in the future.

OPTIMAL FLOW CONTROL PROBLEM IN AIR TRAFFIC CONTROL:PARTⅠ

The fundamental case is considered in which flights from many destinations must be scheduled for arrival at a single congested airport having limited capacities.An air traffic control(ATC)model is developed in this case.A new and efficient algorithm for the optimal solution of ground holding strategy problem(GHSP)is put forward and verified by a numerical example.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

A New Approach to an Automated Air Traffic Control

This paper identifies areas of improvements of the air traffic control system and proposes modification of the concept of automation by using available technologies. With the proposed modification, the current Europe wide en route network structure can be modified in order to make routes more optimal. For this new route network structure, a new concept of automation will be used to manage with the air traffic. The first identified area of improvement is implementation of automation process that will enable decentralization of the air traffic control functionality to each individual aircraft and this will be achieved through automated routing of the aircrafts and CD＆R （conflict detection and resolution）. The FMS （flight management system） at the aircraft will make decisions for the optimal flight route based on the sensor inputs, information on selection of the routes, next hope points and flight levels, all received by ADS-B （automatic dependant surveillance-broadcast）. The second area is processing the information about the deviation from the optimal route as in flight plan due to a traffic management （vectoring, level change） and taking it into consideration when further actions are undertaken. For each action, a cost factor will be calculated from the fuel burned for that action. This factor will be used to select conflict resolution protocol. The proposed concept shall increase the capacity of the network, and enable the air traff^c more efficient and more environmentally friendly while maintaining safe separation.

Traffic Control Under Complex Weather Conditions in Suining Airport

Complex weather conditions is meaning thunderstorm freezing turbulence wind-shear low visibility weather affect the flight safety.When confronted with complex weather conditions,the controllers should know the weather condition and trend weather,and notify the aircraft under your control zone.The controllers provide the required services to the pilots,help the pilots to avoid the complex weather.In this paper,through different complex weathers under different control command,get the different methods of control.

Intelligent Virtual Assistant System(IVAS) for Air Traffic Controllers

Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.

Human Resource Management:A Case Study of the Air Traffic Controller Strike in 1981

The article discusses several hemispheres of human resource management based on a typical case review. The study presents the main problems of the case from both employees and organizations; the issued problems involve compensations and benefits,restructuring,job design,and training. Based on the analysis of the case,two alternative solutions state that the potential routes for the organizations to avoid serious negative results. The study introduces a number of recommendations that can be used as a reference for other organizations to avoid similar risks.

AirNet空管自动化系统提取三汇语音记录仪录音原理研究及模拟软件实现

通过使用wireshark网络抓包工具进行抓包的方式,参考相关接口文档,对AirNet自动化系统提取三汇语音记录仪录音的方法原理进行研究,对AirNet自动化系统及三汇语音记录仪处理录音提取任务的全流程进行梳理,解释了整体的信号传输流程,详细阐述了各接口数据格式、数据类型、具体内容及各字段编码规则,并对实际使用中的注意事项进行了说明。同时,使用Java实现了一个模拟该请求及获取流程的软件,并阐述了该软件的技术原理及各核心功能的实现方式。通过该软件可模拟AirNet自动化系统获取三汇记录仪指定通道及时间段的全流程,同时提供了任务管理能力,用户通过该模拟软件可直接从三汇语音记录仪中提取指定通道及时间段的录音。该方法可作为现有工具的备份手段,满足快速提取三汇记录仪录音的需求。

INNOVATIVE PREDATORY SEARCH ALGORITHM FOR AIRCRAFT ARRIVAL SEQUENCING AND SCHEDULING PROBLEMS

To solve aircraft arrival sequencing and scheduling problems,and improve the typical predatory search algorithm（PSA）,an innovative PSA is developed.The new PSA uses variable constraints of local search and global search to avoid falling into local optimal solutions and the degeneration of solutions.To test the performance of new PSA,a case study with ten arriving flights and two runways is performed.Test results show that the new PSA performs much better than typical PSA and genetic algorithm（GA）in the aspects of the rate of gaining optimal solutions and the computational time.

AUCTIONING METHOD FOR AIRSPACE CONGESTING RESOURCE ALLOCATION AND GAME EQUILIBRIUM ANALYSIS

The airspace congestion is becoming more and more severe.Although there are traffic flow management（TFM）initiatives based on CDM widely applied,how to reschedule these disrupted flights of different airlines integrating TFM initiatives and allocate the limited airspace resources to these airlines equitably and efficiently is still a problem.The air traffic management（ATM）authority aims to minimizing the systemic costs of congested airspaces.And the airlines are self-interested and profit-oriented.Being incorporated into the collaborative decision making（CDM）process,the airlines can influence the rescheduling decisions to profit themselves.The airlines maybe hide the flight information that is disadvantageous to them,but is necessary to the optimal system decision.To realize the coincidence goal between the ATM authority and airlines for the efficient,and equitable allocation of airspace resources,this paper provides an auction-based market method to solve the congestion airspace problem under the pre-tactic and tactic stage of air traffic flow management.Through a simulation experiment,the rationing results show that the auction method can decrease the total delay costs of flights in the congested airspace compared with both the first schedule first service（FSFS）tactic and the ration by schedule（RBS）tactic.Finally,the analysis results indicate that if reallocate the charges from the auction to the airlines according to the proportion of their disrupted flights,the auction mechanism can allocate the airspace resource in economy equitably and decrease the delay losses of the airlines compared with the results of the FSFS tactic.

ATC3在管制员模拟培训中的应用分析

民航的快速发展对管制员的数量和质量都提出了很高的要求,但管制模拟机设备的不足限制了民航院校的管制员培训与培养。通过详细研究管制模拟软件ATC3,并与目前使用的管制模拟机进行对比分析,探讨其创新性及优缺点,进而研究在管制员模拟机培训中的可应用性。同时也针对该软件的缺点提出了一些优化建议,以使该软件很好地应用在管制员模拟培训中,提高管制培训的质量,加快管制员培训的步伐,满足中国民航发展的迫切需求。

基于概率模型的ATC系统冲突目标生成算法

通常用于测试空中交通管制(Air Traffic Control,ATC)自动化系统的飞行冲突告警功能的方法主要有放宽系统告警值和向系统输入模拟的飞行冲突目标的雷达数据。前一种方法存在不可靠性,第二种方法由于只产生简单的确定目标轨迹数据,因此只能简单地测试系统能否告警,无法对系统的飞行冲突预测能力作出评价。为了使用于测试系统的模拟雷达数据更符合实际飞行情况,并检测系统预测飞行冲突的技术水平,本文提出了一种基于飞行冲突概率模型的航迹模拟方法,通过对不同目标对的冲突概率建立数学模型,反推出存在不同冲突概率时目标的运动参数。并通过仿真实验产生了一系列具有不同冲突概率值的目标对的航迹,利用这些数据能对ATC自动化系统进行更有效的冲突探测和告警能力测试。