COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang

Abstract: Highlights•We explore a challenging yet practical problem: GNNs for traffic flow prediction.•Our model conjointly explores spatio-temporal relationships from both prior and posterior views.•Our model introduces a conjoint self-attention decoder that aggregates sequential representations.•Our model uses both multi-rank and multi-scale attention branches to learn representations.•Experiments on the benchmarks demonstrate the effectiveness of the proposed approach.