Score-based Graph Learning for Urban Flow Prediction

Abstract

Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks (GNNs) to deal with the complex dependence between the traffic in adjacent areas. However, existing GNN-based approaches suffer from several critical drawbacks, including improper graph representation of urban traffic data, lack of semantic correlation modeling among graph nodes, and coarse-grained exploitation of external factors. To address these issues, we propose DiffUFP, a novel probabilistic graph-based framework for urban flow prediction. DiffUFP consists of two key designs: 1) a semantic region dynamic extraction method that effectively captures the underlying traffic network topology; and 2) a conditional denoising score-based adjacency matrix generator that takes spatial, temporal, and external factors into account when constructing the adjacency matrix rather than simply concatenation in existing studies. Extensive experiments conducted on real-world datasets demonstrate the superiority of DiffUFP over the state-of-the-art UFP models and the effect of the two specific modules.