Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

Jiahao Ji · Jingyuan Wang · Chao Huang · Junjie Wu · Boren Xu · Zhenhe Wu +2 more

319 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 2019Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction 2015Short-term traffic flow prediction using seasonal ARIMA model with limited input data

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