| Citation: | LIU W，JIA S L. Robust traffic flow prediction based on graph contrastive learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：122-133 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0230
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Robust traffic flow prediction, as the core technology of Intelligent Transportation Systems, is a long-standing but challenging task. The fact that current models need a lot of training data and are susceptible to noise disturbance is a major factor that is restricting the growth of this subject. In academia, graph contrastive learning can alleviate the data-demanding issue and improve the model’s ability to resist data noise through data augmentation and contrastive learning. Therefore, this paper proposes a Traffic Flow prediction framework that incorporates graph contrast learning (TFGCL) for robust traffic flow prediction. The framework has three contributions: First of all, given the unique spatio-temporal characteristics of traffic flow graph (TFG) data, TFGCL proposes three TFG data augmentation methods from the perspective of time and space. Secondly, in order to learn high-quality representations, this work also suggests a filtering method to shield the model from harsh negative samples with identical semantics. Finally, TFGCL jointly trains the traffic flow prediction task and the graph contrastive learning task. Extensive experiments with 8 baselines on 3 real traffic datasets show that the prediction performance of the TFGCL framework is more robust (an improvement of 6.24% compared to the best baseline), especially in datasets with obvious data missing and long-term traffic flow forecasting tasks.
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