A fusion model of temporal graph attention network and machine learning for inferring commuting flow from human activity intensity dynamics

Accurately estimating commuting flow is essential for optimizing urban planning and traffic design. The latest graph neural network (GNN) model with the encoder-decoder-predictor components has several limitations. First, it ignores the temporal dependency of node features for node embedding. Second, different estimation methods used in the decoder and predictor make it difficult to distinguish the contribution of node embedding or estimation method to flow estimation. Third, finer-grained socio-economic features of nodes are difficult to obtain due to low data availability. To address these problems, this study proposes a fusion model of temporal graph attention network and machine learning (TGAT-ML) to infer commuting flow from dynamic human activity intensity distribution. The model first constructs a commuting network with temporal human activity intensity as node features. A temporal graph attention network is then developed to capture the spatiotemporal dependency. The learned node embedding is generated by using a machine learning method in the decoder. Finally, based on learned node embedding and machine learning method used in the decoder, the commuting flow intensity is estimated. Results from an empirical study using the Baidu heat map data of Guangzhou city indicate that the proposed fusion model TGAT-ML outperforms all other baseline models. This study proves that the model performance can be significantly enhanced by determining the edge existence through commuting time-based approach, integrating temporal convolution with graph convolution, and unifying flow estimation method in both decoder and predictor. This work enables commuting flow estimation from dynamic human activity intensity and broadens existing flow generation research in terms of data and methodology.