FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

Xuan Rao, Hao Wang, Liang Zhang, Jing Li, Shuo Shang, Peng Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Traffic flow forecasting plays a vital role in the transportation domain. Existing studies usually manually construct correlation graphs and design sophisticated models for learning spatial and temporal features to predict future traffic states. However, manually constructed correlation graphs cannot accurately extract the complex patterns hidden in the traffic data. In addition, it is challenging for the prediction model to fit traffic data due to its irregularly-shaped distribution. To solve the above-mentioned problems, in this paper, we propose a novel learning-based method to learn a spatial-temporal correlation graph, which could make good use of the traffic flow data. Moreover, we propose First-Order Gradient Supervision (FOGS), a novel method for traffic flow forecasting. FOGS utilizes first-order gradients, rather than specific flows, to train prediction model, which effectively avoids the problem of fitting irregularly-shaped distributions. Comprehensive numerical evaluations on four real-world datasets reveal that the proposed methods achieve state-of-the-art performance and significantly outperform the benchmarks.
Original languageEnglish (US)
Title of host publicationProceedings of the Thirty-First International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence Organization
StatePublished - Jul 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was supported by the NSFC (U2001212, 62032001, and 61932004).


Dive into the research topics of 'FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting'. Together they form a unique fingerprint.

Cite this