🤖 AI Summary
This study addresses limitations in existing air travel demand forecasting models, which typically rely solely on temporal dynamics while neglecting the dual dynamics of intra-flight booking accumulation and inter-flight demand shifts, and often fail to generalize when aircraft types change. To overcome these issues, this work proposes a dual-stream LSTM architecture that integrates self-attention, cross-attention, and hybrid attention mechanisms to jointly model intra-flight booking sequences and fixed-offset patterns across flights for the first time. The model further incorporates residual connections and a gated fusion strategy to enhance generalization. Evaluated on real-world data from Biman Bangladesh Airlines, the proposed approach achieves a mean absolute error (MAE) of 2.8167 and an R² score of 0.9495, significantly outperforming baseline methods, and has already been deployed in operational settings.
📝 Abstract
Accurate short-term demand forecasting is crucial to airline revenue management, yet most existing systems fail to meet this need because current models treat booking data as a single temporal dimension, either the accumulation of bookings for a specific flight or the historical booking profile of the same route. This unidimensional view discards information carried by the other temporal stream and forecasting absolute passenger counts introduces a further operational fragility when change in planned aircraft type alters total seat capacity. This study addresses both limitations. A dual-stream Long Short-Term Memory (LSTM) integrated with attention framework is proposed that simultaneously processes two complementary input sequences: a horizontal sequence capturing intra-flight booking accumulation over the days preceding departure, and a vertical sequence capturing inter-flight booking patterns at fixed days-before-departure offsets across historical flights. Multiple dual-stream architectural variants, combining self-attention, cross-attention, and hybrid attention with concatenation, residual, and gated fusion strategies, are developed and evaluated. Experiments on real-world reservation data from the national airline of Bangladesh, Biman Bangladesh Airlines (BBA), demonstrate that the proposed hybrid model achieves a Mean Absolute Error of 2.8167 and a coefficient of determination ($R^{2}$) of 0.9495, outperforming single-stream baselines, tree-based models, and three prior dual-LSTM architectures applied to the same data. Validation across four flight category pairs; domestic versus international, direct versus transit, high versus low frequency, and short versus mid versus long haul confirms that the model generalizes across operationally diverse route types. Biman Bangladesh Airlines (BBA) has officially integrated this methodology into its operations.