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

There 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Liu, Zibo

Chair dc:contributor.committeechair

• Reddy, Chandan K.

Committee members dc:contributor.committeemember

• Zhou, Dawei
• Subbian, Vignesh

• Traffic Forecasting
• Neural ODE
• Attention Mechanism

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:36166

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/112962