Airspace-aware Contingency Landing Planning

Abstract

This paper develops a real-time, search-based aircraft contingency landing planner that minimizes traffic disruptions while accounting for ground risk. The airspace model captures dense air traffic departure and arrival flows, helicopter corridors, and prohibited zones and is demonstrated with a Washington, D.C., area case study. Historical Automatic Dependent Surveillance-Broadcast (ADS-B) data are processed to estimate air traffic density. A low-latency computational geometry algorithm generates proximity-based heatmaps around high-risk corridors and restricted regions. Airspace risk is quantified as the cumulative exposure time of a landing trajectory within congested regions, while ground risk is assessed from overflown population density to jointly guide trajectory selection. A landing site selection module further mitigates disruption to nominal air traffic operations. Benchmarking against minimum-risk Dubins solutions demonstrates that the proposed planner achieves lower joint risk and reduced airspace disruption while maintaining real-time performance. Under airspace-risk-only conditions, the planner generates trajectories within an average of 2.9 seconds on a laptop computer. Future work will incorporate dynamic air traffic updates to enable spatiotemporal contingency landing planning that minimizes the need for real-time traffic rerouting.

Figures (19)

• Figure 1: Normalized population density around Washington, D.C.
• Figure 2: Ronald Reagan Washington National Airport, North flow ADS-B flight trajectories - Jan. 29, 2025, from 12:07 p.m. to 8:49 p.m. local time.
• Figure 3: Ronald Reagan Washington National Airport air traffic heatmap.
• Figure 4: DCA area helicopter routes and no-fly zones modeled as polyhedra.
• Figure 5: A polyhedron face defined by its edges, vertices, and normal vector, along with the relative positions of an arbitrary point with respect to the face.