Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

Zhang, Lingyu1,2,3; Geng, Xu4; Qin, Zhiwei2; Wang, Hongjun5; Wang, Xiao2,3; Zhang, Ying2,3; Liang, Jian2,3; Wu, Guobin2,3; Song, Xuan5; Wang, Yunhai1

2022-10-01

10.3390/su141912397

SUSTAINABILITY   Impact Factor & Quartile

2071-1050

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, such as region-wise distance or functional similarity. To incorporate multiple relationships into a 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 the generalization error by reinforcing the learning of multi-modal 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 a 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 a ride-hailing 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.

multi-modal machine learning graph convolution networks multi-task learning transfer learning

SCI ; SSCI

English

Others

National Key Research and Development Program of China[2019YFB1600300]

Science & Technology - Other Topics ; Environmental Sciences & Ecology

Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies

WOS:000867106600001

MDPI

Web of Science

1.Shandong Univ, Sch Comp Sci & Technol, Qingdao 250012, Peoples R China
2.Didi Chuxing, Beijing 065001, Peoples R China
3.Draweast Tech, Data Sci & Artificial Intelligence Dept, Beijing 065001, Peoples R China
4.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong 999077, Peoples R China
5.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China

Zhang, Lingyu,Geng, Xu,Qin, Zhiwei,et al. Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting[J]. SUSTAINABILITY,2022,14(19).

Zhang, Lingyu.,Geng, Xu.,Qin, Zhiwei.,Wang, Hongjun.,Wang, Xiao.,...&Wang, Yunhai.(2022).Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting.SUSTAINABILITY,14(19).

Zhang, Lingyu,et al."Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting".SUSTAINABILITY 14.19(2022).