Just Accepted
Tucker Decomposition Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction
Authors: 
Genan Dai, Weiyang Kong, Yubao Liu, Bowen Zhang, Xiaojiang Peng, Xiaomao Fan, Hu HuangAuthors Info & Claims
Accepted on 08 November 2024
Abstract
Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in addressing this problem. However, GCN-based models were often based on predefined crowd-flow graphs via historical movement behaviors of human beings and traffic vehicles, which ignored the abnormal changes in crowd flows. In this study, we propose a multi-scale fusion GCN-based framework with Tucker decomposition named mTDNet to enhance dynamic GCN for Crowd flows prediction. Following the paradigm of extant methods, we also employ the predefined crowd-flow graphs as a part of mTDNet to effectively capture the historical movement behaviors of crowd flows. To capture the abnormal changes, we propose a Tucker decomposition-based network with the product of the adjacency matrix of historical movement pattern graphs and an adaptive learning tensor (\(ALT\)) by reconstructing the crowd flows. Particularly, we utilize the Tucker decomposition scheme to decompose \(ALT\), which enhances the dynamic learning of graph structures, allowing for effective capturing of the dynamic changes in crowd flow, including abnormal changes. Furthermore, a multi-scale three-dimensional GCN is utilized to mine and fuse the multiscale spatio-temporal information from crowd flows, to further boost the mTDNet prediction performance. Experiments conducted on two real-world datasets showed that the proposed mTDNet surpasses other crowd flow prediction methods.
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Index Terms
- Tucker Decomposition Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction
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EISSN:2157-6912
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Publication History
Online AM: 02 December 2024
Accepted: 08 November 2024
Revised: 02 May 2024
Received: 31 August 2023
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