Building Transportation Foundation Model via Generative Graph Transformer
Xuhong Wang, Ding Wang, Chen Liang, Fei–Yue Wang, Yilun Lin
Abstract
In recent years, researchers have made notable advancements in various disciplines using large-scale foundation models. However, foundation models in the transportation system have not received adequate attention. To address this gap, we propose the Generative Graph Transformer (GGT), a transportation foundation model (TFM) that leverages graph structure and dynamic graph generation algorithms. The primary objective of our TFM is to capture participant behavior and interaction in the transportation system, at various scales, and establish a large-scale neural network to comprehend the entire system. The GGT-based TFM can overcom challenges of structural complexity and model accuracy in conventional traffic models. This approach holds promise for addressing complex traffic issues by utilizing up-to-date real traffic data. To demonstrate the capabilities of GGT, a simulation experiment was conducted.