Luo, Ruikang (School of Electrical and Electronic Engineering, Nanyang Technol), Song, Yaofeng (Nanyang Technological University), Lu, Yun (Nanyang Technological University), Zhao, Nanbin (Nanyang Technological University), Su, Rong (Nanyang Technological University)

STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting

Scheduled for presentation during the Invited Session "Emerging Data-driven Technologies and Machine Intellection for Smart Traffic Applications" (FrAT6), Friday, September 27, 2024, 11:30−11:50, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurate long series forecasting of traffic information is critical for the development of intelligent traffic systems. We may benefit from the rapid growth of neural network analysis technology to better understand the underlying functioning patterns of traffic networks as a result of this progress. Due to the fact that traffic data and facility utilization circumstances are sequentially dependent on past and present situations, several related neural network techniques based on temporal dependency extraction models have been developed to solve the problem. The complicated topological road structure, on the other hand, amplifies the effect of spatial interdependence, which cannot be captured by pure temporal extraction approaches. Additionally, the typical Deep Recurrent Neural Network (RNN) topology has a constraint on global information extraction, which is required for comprehensive long-term prediction. This study proposes a new spatial-temporal neural network architecture, called Spatial-Temporal Graph-Informer (STGIN), to handle the long-term traffic parameters forecasting issue by merging the Informer and Graph Attention Network (GAT) layers for spatial and temporal relationships extraction. The attention mechanism potentially guarantees long-term prediction performance without significant information loss from distant inputs. On two real-world traffic datasets with varying horizons, experimental findings validate the long sequence prediction abilities, and further interpretation is provided.