SimMF: A Simplified Multi-Factor Modeling Framework for Fine-Grained Urban Flow Inference

Fine-grained urban flow inference is pivotal in alleviating traffic congestion and reducing detector deployment costs. It aims to infer fine-grained flow maps from coarse-grained traffic data. However, existing methods face challenges due to the highly complex nature of spatial modeling for urban flow patterns and the distinctive impact of external factors such as temperature and weather. To address these issues, this paper proposes a Simplified Multi-Factor spatial modeling framework (SimMF) to enhance the accuracy of fine-grained flow inference while optimizing inference complexity. SimMF incorporates a dual-path architecture for short-range modeling, combining multi-scale convolutions and frequency-domain processing via FFT to capture cross-scale spatial correlations and heterogeneity. For long-range dependencies, SimMF employs enhanced bottleneck attention with linear complexity, effectively modeling intricate spatial relationships. Additionally, SimMF adopts a view-aware learnable approach to represent external factors, enabling each factor to generate distinctive feature maps and capture its unique characteristics. Experimental results on two urban datasets demonstrate that SimMF outperforms existing methods, achieving superior inference accuracy while maintaining computational efficiency with significantly improved computational efficiency.