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

Lingyu Zhang, Xu Geng, Zhiwei Qin, Hongjun Wang, Xiao Wang, Ying Zhang, Jian Liang, Guobin Wu, Xuan Song and Yunhai Wang ()
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Lingyu Zhang: School of Computer Science and Technology, Shandong University, Qingdao 250012, China
Xu Geng: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hongkong 999077, China
Zhiwei Qin: Didi Chuxing, Beijing 065001, China
Hongjun Wang: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Xiao Wang: Didi Chuxing, Beijing 065001, China
Ying Zhang: Didi Chuxing, Beijing 065001, China
Jian Liang: Didi Chuxing, Beijing 065001, China
Guobin Wu: Didi Chuxing, Beijing 065001, China
Xuan Song: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Yunhai Wang: School of Computer Science and Technology, Shandong University, Qingdao 250012, China

Sustainability, 2022, vol. 14, issue 19, 1-17

Abstract: 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.

Keywords: multi-modal machine learning; graph convolution networks; multi-task learning; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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