Airport passenger flow optimization spans terminal-internal processes (check-in, security, boarding, baggage), landside ground access (taxis, buses, rail, private vehicles), airside surface operations (taxiway routing, gate management, departure metering), and network-level air traffic flow management. The field bifurcates into two broad pillars: predictive analytics — forecasting passenger and aircraft volume in space and time — and prescriptive optimization — allocating gates, staff, vehicles, and taxiway resources in real or near-real time.

Core technical mechanisms identified across retrieved records include queueing-theoretic models for passenger congestion at processing checkpoints, discrete-event simulation (DES) for terminal and landside logistics, deep learning architectures (LSTM, GCN, ResNet combinations) for spatiotemporal flow prediction, and mixed-integer linear/integer programming (MILP/ILP) for gate assignment and surface scheduling. Big data fusion integrates flight schedules, weather, historical delay rates, and passenger mobility data, while machine learning-enhanced optimization combines learned patterns with operational scheduling.

The literature dataset spans 2009–2023, anchoring the analytical foundation in peer-reviewed research from institutions including DLR Germany, Eurocontrol, and NASA. The most recent patent filings date to early 2026, signaling active, ongoing innovation. PatSnap’s IP analytics platform was used to surface and analyse these records.

AI-first optimization is now the competitive baseline. Deep learning architectures combining graph convolution (for network topology) and recurrent networks (for temporal dynamics) represent the emergent standard for airport flow prediction. R&D teams without GCN-LSTM or equivalent hybrid capability are already behind the frontier demonstrated in this dataset. PatSnap’s solutions for tracking emerging technology vectors can help teams map their position against this benchmark.

Schedule independence is a strategic differentiator. Boeing’s 2025 approach to congestion monitoring without proprietary schedule data (using global ADS-B streams and KDE models) represents an IP moat for systems that must scale across hundreds of airports. IP strategists should map white space around schedule-independent, globally portable prediction architectures. The PatSnap analytics platform supports this kind of white-space analysis at scale.

China’s institutional R&D pipeline is the most active. NUAA alone accounts for 4 active patents, with filings progressing from national flow prediction (2016) to multi-airport GCN-LSTM (2023) and real-time taxiway intelligence (2025). Combined with CETC, Civil Aviation Flight University, and commercial filers, Chinese organizations represent the most cohesive filing bloc in this dataset. Non-Chinese players entering this space should assess freedom-to-operate carefully in CN jurisdiction — a process supported by PatSnap’s customer case studies in competitive IP intelligence.

The passenger-facing closed loop is underexplored commercially but patent-active. The emergence of real-time dynamic route recommendations pushed to passengers’ mobile devices (Shenzhen Taiyi Chuanxin, 2025–2026) signals a convergence between back-end flow optimization and front-end passenger UX. Advanced Air Mobility creates a greenfield optimization domain — urban air taxi and on-demand AAM services will require entirely new passenger flow management infrastructure, from vertiport-level queueing to city-scale demand forecasting. Standards bodies such as ICAO and research institutions like Eurocontrol are beginning to address this gap. The 2024 filings from Civil Aviation Flight University of China are among the first to formally extend airport flow optimization methods into this domain, suggesting first-mover IP opportunity remains available.