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<p>With global skies busier than ever and air traffic management (ATM) systems becoming increasingly complex, air traffic controllers (ATCOs) now face unprecedented demands on their performance and workload management. Predicting accurate ATCO’s workload assessment is essential to maintain safety, improve operational efficiency, and ensure optimal resource utilization in aviation. This paper presents a comprehensive systematic review of workload prediction approaches for ATCOs. A structured search was conducted across four major scientific databases—Web of Science, Scopus, IEEE Xplore, and SpringerLink—covering publications from 2000 to 2024. The search initially identified 374 records, of which 70 studies met the inclusion criteria and were analyzed in detail. We systematically analyze and categorize influential workload factors such as task complexity, airspace structure, operational limitations, and cognitive load. The review also explores state-of-the-art methods, particularly those utilizing artificial intelligence (AI) and machine learning (ML), with a focus on their ability to process multimodal data and respond to dynamic operational conditions. We discuss practical implications of workload prediction models in enhancing safety, supporting adaptive ATM systems, and improving personnel deployment. Additionally, the survey identifies existing research gaps, including issues in integrating diverse data sources, addressing ethical considerations, and adapting to innovations like unmanned aerial vehicles (UAVs) and urban air mobility (UAM). To advance the field, we outline future research directions centered on real-time, adaptive prediction systems, improved workload measurement tools, and effective human–machine collaboration. This comprehensive review aims to support researchers and practitioners by providing a foundational resource for developing next-generation workload prediction frameworks in modern air traffic environments.</p>

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Workload prediction for air traffic controllers: a comprehensive review of challenges, techniques, and future prospects

  • 期刊论文

  • 发表日期：

    2026年4月26日

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  • Tangina Sultana,
  • Md. Rafiul Islam Rifat,
  • Md. Firoj Mahmud,
  • Md. Delowar Hossain,
  • Md. Emran Biswas,
  • Ga-Won Lee,
  • Eui-Nam Huh

摘要

With global skies busier than ever and air traffic management (ATM) systems becoming increasingly complex, air traffic controllers (ATCOs) now face unprecedented demands on their performance and workload management. Predicting accurate ATCO’s workload assessment is essential to maintain safety, improve operational efficiency, and ensure optimal resource utilization in aviation. This paper presents a comprehensive systematic review of workload prediction approaches for ATCOs. A structured search was conducted across four major scientific databases—Web of Science, Scopus, IEEE Xplore, and SpringerLink—covering publications from 2000 to 2024. The search initially identified 374 records, of which 70 studies met the inclusion criteria and were analyzed in detail. We systematically analyze and categorize influential workload factors such as task complexity, airspace structure, operational limitations, and cognitive load. The review also explores state-of-the-art methods, particularly those utilizing artificial intelligence (AI) and machine learning (ML), with a focus on their ability to process multimodal data and respond to dynamic operational conditions. We discuss practical implications of workload prediction models in enhancing safety, supporting adaptive ATM systems, and improving personnel deployment. Additionally, the survey identifies existing research gaps, including issues in integrating diverse data sources, addressing ethical considerations, and adapting to innovations like unmanned aerial vehicles (UAVs) and urban air mobility (UAM). To advance the field, we outline future research directions centered on real-time, adaptive prediction systems, improved workload measurement tools, and effective human–machine collaboration. This comprehensive review aims to support researchers and practitioners by providing a foundational resource for developing next-generation workload prediction frameworks in modern air traffic environments.