  Graph convolution network based approaches have been recently used to model
region-wise relationships in region-level prediction problems in urban
computing. Each relationship represents a kind of spatial dependency, like
region-wise distance or functional similarity. To incorporate multiple
relationships into spatial feature extraction, we define the problem as a
multi-modal machine learning problem on multi-graph convolution networks.
Leveraging the advantage of multi-modal machine learning, we propose to develop
modality interaction mechanisms for this problem, in order to reduce
generalization error by reinforcing the learning of multimodal coordinated
representations. In this work, we propose two interaction techniques for
handling features in lower layers and higher layers respectively. In lower
layers, we propose grouped GCN to combine the graph connectivity from different
modalities for more complete spatial feature extraction. In higher layers, we
adapt multi-linear relationship networks to GCN by exploring the dimension
transformation and freezing part of the covariance structure. The adapted
approach, called multi-linear relationship GCN, learns more generalized
features to overcome the train-test divergence induced by time shifting. We
evaluated our model on ridehailing demand forecasting problem using two
real-world datasets. The proposed technique outperforms state-of-the art
baselines in terms of prediction accuracy, training efficiency,
interpretability and model robustness.
