Human-Centered Decision Support in Future Air Traffic Management

Dear Colleagues,

Rapid growth of global air traffic, urban air mobility (UAM), unmanned aircraft systems (UAS), and autonomous aircraft, along with pervasive AI applications, are reshaping air traffic management (ATM). Current airspace operations rely heavily on human controllers, while most existing studies target fully automated ATM frameworks. The inconsistency between research settings and real operational constraints greatly restricts the practical deployment of newly developed ATM systems. Traditional automation-driven decision support tools ignore operators’ cognitive limits, workload, situational awareness and system trust, leading to potential safety risks and low operational efficiency.

This Topic welcomes diverse studies on human-centered design for next-generation ATM, covering human-autonomy collaboration, explainable decision aids, human–machine interface optimization, human-in-the-loop conflict resolution, adaptive load regulation and standardized evaluation systems. Accepted works may also address UAM-airspace coordination, drone fleet integration, multi-party collaborative decision-making and rules balancing automation and human supervision. This collection integrates real operational constraints with intelligent ATM methodologies, expands the applicable scope of advanced ATM frameworks, and develops safer, robust, deployable airspace architectures for future complex traffic scenarios.

Dr. Yutong Chen
Dr. Clark Borst
Topic Editors