Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

12/07/2022
by   Jiahao Ji, et al.
0

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.

READ FULL TEXT

page 5

page 6

research
06/19/2023

Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation

Spatial-temporal graph learning has emerged as a promising solution for ...
research
03/07/2022

HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data

Traffic accident forecasting is a significant problem for transportation...
research
12/10/2022

Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Spatio-temporal machine learning is critically needed for a variety of s...
research
10/27/2019

The Quo Vadis submission at Traffic4cast 2019

We describe the submission of the Quo Vadis team to the Traffic4cast com...
research
06/14/2022

Involution game with spatio-temporal heterogeneity of social resources

When group members claim a portion of limited resources, it is tempting ...
research
04/18/2022

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

Crime has become a major concern in many cities, which calls for the ris...
research
07/29/2020

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

Behavioral changes are the earliest signs of a mental disorder, but argu...

Please sign up or login with your details

Forgot password? Click here to reset