Building Transportation Foundation Model via Generative Graph
  Transformer
Xuhong Wang Ding Wang Liang Chen Yilun Lin
Efficient traffic management is crucial for maintaining urban mobility,
especially in densely populated areas where congestion, accidents, and delays
can lead to frustrating and expensive commutes. However, existing prediction
methods face challenges in terms of optimizing a single objective and
understanding the complex composition of the transportation system. Moreover,
they lack the ability to understand the macroscopic system and cannot
efficiently utilize big data. In this paper, we propose a novel approach,
Transportation Foundation Model (TFM), which integrates the principles of
traffic simulation into traffic prediction. TFM uses graph structures and
dynamic graph generation algorithms to capture the participatory behavior and
interaction of transportation system actors. This data-driven and model-free
simulation method addresses the challenges faced by traditional systems in
terms of structural complexity and model accuracy and provides a foundation for
solving complex transportation problems with real data. The proposed approach
shows promising results in accurately predicting traffic outcomes in an urban
transportation setting.