arXiv:2605.10083v2 Announce Type: replace Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated time series. However, traffic dynamics are governed by aircraft states and their interactions in continuous airspace. Such aggregation obscures fine-grained information, including aircraft kinematics, boundary interactions, and control intent. Here we present AeroSense, a state-to-flow modeling paradigm that predicts future traffic flow directly from instantaneous air
The increasing sophistication of AI models and the availability of granular real-time data from air traffic systems enable more precise microscopic modeling approaches.
Improved air traffic flow prediction using AI can significantly enhance the efficiency, safety, and capacity of global air travel, directly impacting economies and supply chains.
Air traffic management shifts from aggregate, reactive control to proactive, micro-level intent and interaction-based prediction, optimizing airspace utilization.
- · Airlines
- · Air traffic control agencies
- · Aerospace technology providers
- · Logistics companies
- · Traditional air traffic management software
- · Airports with limited capacity due to inefficient flow
Reduced flight delays and fuel consumption become immediate benefits for air carriers and passengers.
Increased airspace capacity could alleviate bottlenecks at major airports and permit more frequent flight schedules.
The application of microscopic state modeling could extend to other complex logistic systems, revolutionizing supply chain optimization and autonomous vehicle coordination.
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Read at arXiv cs.LG