Multimodal Disentangled Dynamic Fusion for Short-Term Multimodal Travel Demand Forecasting

Short-term multimodal travel demand forecasting must reconcile shared mobility patterns with mode-specific dynamics and uneven spatial coverage. This paper proposes Multimodal Disentangled Dynamic Fusion (MDDF), which combines three mechanisms: dual-branch encoding with an orthogonality constraint to separate shared and modality-specific representations; Jensen–Shannon divergence regularization with coverage-aware masking to align shared features without introducing structurally absent observations; and attention-based dynamic fusion with an auxiliary ranking loss to adapt cross-modal information exchange. We evaluate MDDF over five random seeds on two multimodal datasets with distinct settings: 10 min bus, metro, and taxi demand over 491 traffic analysis zones in Shenzhen, and hourly bus, metro, and bike demand over 69 zones in Manhattan. MDDF achieves the lowest mean MAE in all nine mode–horizon settings on both datasets and the lowest mean RMSE in eight of nine settings on each dataset. On Shenzhen, MDDF improves upon the strongest competing baseline in all nine mode–horizon settings; at the 60 min horizon, the mean MAE reductions are 19.5% for bus, 18.3% for metro, and 5.9% for taxi. Ablation and representation analyses indicate broad, though not universal, benefits from disentanglement, alignment, and dynamic fusion.