DeepAI AI Chat
Log In Sign Up

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

by   Li Mengzhang, et al.

Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. To overcome those limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer, SFTGNN could handle long sequences. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.


page 1

page 2

page 5


Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for help...

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Spatial-temporal graph modeling is an important task to analyze the spat...

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

We all depend on mobility, and vehicular transportation affects the dail...

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Accurate forecasting of citywide traffic flow has been playing critical ...

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

We study the forecasting problem for traffic with dynamic, possibly peri...

A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields

Self-awareness is the key capability of autonomous systems, e.g., autono...

PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting

The complex spatial-temporal correlations in transportation networks mak...