AttnOD: An Attention-Based OD Prediction Model with Adaptive Graph Convolution

In recent years, with the continuous growth of traffic scale, the prediction of passenger demand has become an important problem. However, many of the previous methods only considered the passenger flow in a region or at one point, which cannot effectively model the detailed demands from origins to destinations. Differently, this paper focuses on a challenging yet worthwhile task called Origin-Destination (OD) prediction, which aims to predict the traffic demand between each pair of regions in the future. In this regard, an Attention-based OD prediction model with adaptive graph convolution (AttnOD) is designed. Specifically, the model follows an Encoder-Decoder structure, which aims to encode historical input as hidden states and decode them into future prediction. Among each block in the encoder and the decoder, adaptive graph convolution is used to capture spatial dependencies, and self-attention mechanism is used to capture temporal dependencies. In addition, a cross attention module is designed to reduce cumulative propagation error for prediction. Through comparative experiments on the Beijing subway and New York taxi datasets, it is proved that the AttnOD model can obtain better performance than the baselines under most evaluation indicators. Furthermore, through the ablation experiments, the effect of each module is also verified.