Spatial-Temporal Attention Simple Graph Neural Network

Abstract

The growth of the autonomous driving industry in recent years has spurred research on intelligent transportation systems. However, predicting long-term traffic patterns is a complex task that can lead to overfitting and fluctuations in model predictions.To address these challenges, this paper proposes a spatio-temporal modeling approach that captures both the spatial and temporal features of traffic data. The method fuses these features using a gated fusion mechanism and then applies feedforward neural networks to transform the spatio-temporal data into predictions for future time steps.To mitigate overfitting, the paper introduces a novel loss function called the mean loss function. By minimizing fluctuations in model predictions, this approach aims to improve the accuracy of long-term traffic forecasts.Overall, this paper presents a promising approach to improving the performance of intelligent transportation systems, particularly in the area of long-term traffic prediction. The proposed method combines several techniques, including spatio-temporal modeling, neural networks, and a new loss function,to address the challenges of overfitting and prediction fluctuations.After conducting multiple experiments on the publicly available transportation network datasets, METR-LA and PEMS-Bay, our proposed model demonstrated improved performance in long-term traffic flow prediction