Traffic Prediction Using Graph Neural Network

Traffic prediction is an important and challenging task in transportation management. Accurately predicting traffic patterns is crucial for reducing congestion, improving safety, and optimizing travel time. In recent years, graph neural networks (GNNs) have shown great potential in predicting traffic by taking into account the graph structure of road networks. In this project, we propose to develop a traffic prediction system using GNNs. The proposed system consists of three main components: data pre-processing, model development, and model evaluation. We first collect traffic data from various sources and pre-process it to remove noise and inconsistencies, and convert it into a suitable format for GNNs. We then develop a GNN model designed to capture the graph structure of the traffic network and use it to predict traffic patterns for a given time interval. The proposed model is trained and optimized using a loss function and evaluated using standard metrics such as root mean squared error (RMSE). The proposed traffic prediction system has the potential to significantly improve traffic management and reduce congestion by providing accurate traffic predictions. The GNN model can capture complex traffic patterns and dependencies between different parts of the road network, leading to more accurate predictions. Overall, this project aims to provide a better understanding of the potential of GNNs in traffic prediction and contribute to the development of more efficient and effective transportation management systems