  Traffic prediction is pivotal for rational transportation supply scheduling
and allocation. Existing researches into short-term traffic prediction,
however, face challenges in adequately addressing exceptional circumstances and
integrating non-numerical contextual information like weather into models.
While, Large language models offer a promising solution due to their inherent
world knowledge. However, directly using them for traffic prediction presents
drawbacks such as high cost, lack of determinism, and limited mathematical
capability. To mitigate these issues, this study proposes a novel approach.
Instead of directly employing large models for prediction, it utilizes them to
process textual information and obtain embeddings. These embeddings are then
combined with historical traffic data and inputted into traditional
spatiotemporal forecasting models. The study investigates two types of special
scenarios: regional-level and node-level. For regional-level scenarios, textual
information is represented as a node connected to the entire network. For
node-level scenarios, embeddings from the large model represent additional
nodes connected only to corresponding nodes. This approach shows a significant
improvement in prediction accuracy according to our experiment of New York Bike
dataset.
