Improving Traffic Density Forecasting in Intelligent Transportation
  Systems Using Gated Graph Neural Networks
This study delves into the application of graph neural networks in the realm
of traffic forecasting, a crucial facet of intelligent transportation systems.
Accurate traffic predictions are vital for functions like trip planning,
traffic control, and vehicle routing in such systems. Three prominent GNN
architectures Graph Convolutional Networks (Graph Sample and Aggregation) and
Gated Graph Neural Networks are explored within the context of traffic
prediction. Each architecture's methodology is thoroughly examined, including
layer configurations, activation functions,and hyperparameters. The primary
goal is to minimize prediction errors, with GGNNs emerging as the most
effective choice among the three models. The research outlines outcomes for
each architecture, elucidating their predictive performance through root mean
squared error and mean absolute error (MAE). Hypothetical results reveal
intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while
GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph
Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE
of 7.1, positioning them as the frontrunner.
Razib Hayat Khan, Jonayet Miah, S M Yasir Arafat, M M Mahbubul Syeed, Duc M Ca
Razib Hayat Khan

###### Abstract

This study delves into the application of Graph Neural Networks (GNNs) in the realm of traffic forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions are vital for functions like trip planning, traffic control, and vehicle routing in such systems. Three prominent GNN architectures--Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)--are explored within the context of traffic prediction. Each architecture's methodology is thoroughly examined, including layer configurations, activation functions, and hyperparameters. The primary goal is to minimize prediction errors, with GGNNs emerging as the most effective choice among the three models. The research outlines outcomes for each architecture, elucidating their predictive performance through Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hypothetical results reveal intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE of 7.1, positioning them as the frontrunner. However, the study acknowledges result variability, emphasizing the influence of factors like dataset characteristics, graph structure, feature engineering, and hyperparameter tuning.

_Keywords_- Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)

## I Introduction

In the realm of intelligent transportation systems (ITS), accurate traffic forecasting stands as a cornerstone, shaping pivotal tasks like trip planning, traffic control, and vehicle routing. The efficacy of these applications hinges on the prowess of predictive models, and within this context, Graph Neural Networks (GNNs) have emerged as a groundbreaking approach. With an inherent capacity to leverage graph structures intrinsic to traffic systems, GNNs have garnered significant attention as a potent tool for traffic prediction. This study delves into the application of three prominent GNN architectures--Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)--in the specific context of traffic forecasting [1, 24].

In the dynamic landscape of intelligent transportation systems (ITS), the accurate prediction of traffic patterns holds an irreplaceable significance. These predictions drive critical functions such as efficient trip planning, effective traffic control strategies, and optimized vehicle routing. Amid this backdrop, Graph Neural Networks (GNNs) have emerged as a transformative approach, harnessing the inherent structure of transportation networks to enhance traffic forecasting. This study delves into the realm of traffic prediction, exploring the application of three pivotal GNN architectures: Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs).

The core of this investigation lies in the comprehensive examination of each architecture's methodology, encompassing the intricate interplay of layer configurations, activation functions, and hyperparameters. The ultimate objective is to minimize the errors inherent in traffic predictions, guided by the tenets of accuracy and efficacy. Among the trio of GNN models explored, Gated Graph Neural Networks