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Bootstrapping on Continuous-Time Dynamic Graphs for Crowd Flow Modeling
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DOI：10.1109/TKDE.2024.3383663.png)
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Abstract
En 中文
Numerous spatial-temporal learning methods have been proposed for crowd flow modeling, which is an important problem in Intelligent Transportation Systems (ITSs). However, most of the existing methods were designed to use data in one specific form to solve one particular task of crowd flow modeling and the shared patterns among different tasks have been largely ignored. In this paper, we investigate how to learn generic node representations that can simultaneously support various downstream tasks of crowd flow modeling. Along this line, we develop a continuous-time dynamic graph representation learning method based on Bootstrapping for Crowd Flow modeling (BootCF). Our approach follows a training procedure with two phases. In the pre-training phase, the continuous-time dynamic encoder converts edges with timestamps into messages to update the representations of the related traffic nodes. Inspired by the recent progress of contrastive learning, a bootstrapping framework for continuous-time dynamic graphs is designed to calculate pre-training loss and update the model in a self-supervised way, and thus enabling the node representation learning to be task-agnostic. Moreover, a context-aware data augmentation on continuous-time dynamic graphs is proposed to generate the augmented view of input data. Once the general node representations are obtained, the second phase can learn an effective model for any downstream task. Experiments on two real-world datasets show that our approach can achieve significant performance gain on four downstream tasks, which demonstrates that the proposed method has the powerful generalization capability for learning task-agnostic node representations.
Keywords:
Task analysis
Data models
Representation learning
Predictive models
Integrated circuit modeling
Roads
Estimation
Crowd flow modeling
continuous-time dynamic graph
representation learning
contrastive learning
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
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