Developing Novel Features Employed by Regression Methods to Solve Air Traffic Delay Prediction Problems - Tran, Loc; Nguyen, Luong Minh Tri; Nguyen, Huu Hiep; Tran, Tan Phuc; Phan, Kim Anh; Tran, Linh (Engineering, Technology & Applied Science Research, 2026)
Flight delays are influenced not only by flight-specific factors but also by network effects, where disruptions at central airports propagate through downstream connections. This study proposes a lightweight, model-agnostic framework that injects network structure into standard flight-level regression for arrival delay prediction. Using Bureau of Transportation Statistics (BTS) on-time performance data (523,435 completed flights from 2025-01-01 to 2025-01-31), the study builds (i) a directed airport-route graph weighted by observed origin–destination counts and (ii) a rotation hypergraph in which each (tail number, day) forms a hyperedge connecting all airports visited, weighted by the number of legs. From these structures, four airport-level ranking signals are derived: Graph PageRank (GPR), GCN-Style Fixed-Point Ranking (GGCN), Hypergraph PageRank (HPR), and Hypergraph Fixed-Point Ranking (HGCN). Origin and destination ranking features are attached to each flight, and Linear Regression (LR), K-Nearest Neighbors (KNN) regression, and Multi-Layer Perceptron (MLP) regression models are evaluated under a time-aware split. Network-enriched features yield modest but consistent gains, with the best performance achieved by MLP using all ranking features.