Speed Prediction for Freight Transportation: A Graph Neural Network Approach

Abstract

The accuracy in predicting the speed of freight within a transportation network is a cornerstone for achieving optimality in logistics and supply chain management. Given the critical role of freight transport in maintaining the efficiency of global supply chains, this study highlights the necessity for innovative predictive models capable of navigating the multifaceted and dynamic nature of transportation systems. Traditional methodologies, primarily rooted in statistical analysis and linear modeling, have provided foundational insights but are not adequate in addressing the multifaceted and dynamic nature of modern transportation systems. This research proposes a three-fold approach, firstly, analyzing conventional traffic speed prediction methodologies to understand their theoretical foundations, data utilization, and performance metrics and further, advancing a GNN-based model that effectively captures the complex relationships and dynamics within road networks to enhance the precision of vehicular speed predictions. This study aims to contribute to the domain of transportation and freight logistics by offering a robust, adaptive, and accurate tool for speed prediction, leveraging advanced techniques such as diffusion-convolutional layers and multi-head attention mechanisms integrated with Gated Recurrent Units (GRUs). This novel approach advances conventional models by providing superior predictive accuracy and efficiency, thus addressing key challenges such as the need for real-time adaptability and the ability to capture intricate network dynamics. By enhancing the precision of freight speed predictions, the study not only aims to improve the operational efficiency of freight networks but also to contribute to the optimization of global trade and supply chain management, ultimately supporting the resilience and reliability of global logistics operations. Importantly, the analysis assumes that the average speeds recorded in the METR-LA dataset, which primarily reflects mixed vehicular traffic, are representative of freight vehicles, providing a basis for generalizing findings to freight transport scenarios.