Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting

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dc.contributor.authorLiu, Ziboen
dc.contributor.committeechairReddy, Chandan K.en
dc.contributor.committeememberZhou, Daweien
dc.contributor.committeememberSubbian, Vigneshen
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2022-12-21T09:00:14Zen
dc.date.available2022-12-21T09:00:14Zen
dc.date.issued2022-12-20en
dc.description.abstractThere is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations (NODEs). The existing graph ODE models are still limited in feature extraction due to (1) having bias towards global temporal patterns and ignoring local patterns which are crucial in case of unexpected events; (2) missing dynamic semantic edges in the model architecture; and (3) using simple aggregation layers that disregard the high-dimensional feature correlations. In this thesis, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations by capturing different views of complex local and global dynamic spatio-temporal dependencies. We also add some techniques to further improve the communication between different ODE-GNN modules towards the forecasting task. Extensive experiments conducted on four real-world datasets demonstrate the outperformance of GRAM-ODE compared with state-of-the-art baselines as well as the contribution of different GRAM-ODE components to the performance.en
dc.description.abstractgeneralThere is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. In traffic forecasting, current works limited in correctly capturing the key correlation of spatial and temporal patterns. In this thesis, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) to tackle the problem by using the separate ODE modules to deal with spatial and temporal patterns and further improve the communication between different modules. Extensive experiments conducted on four real-world datasets demonstrate the outperformance of GRAM-ODE compared with state-of-the-art baselines.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:36166en
dc.identifier.urihttp://hdl.handle.net/10919/112962en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTraffic Forecasting; Neural ODE; Attention Mechanismen
dc.titleGraph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecastingen
dc.typeThesisen
thesis.degree.disciplineComputer Science & Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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