Enhancing Taxi Demand Prediction with Limited Data using a Spatial-Temporal Large Language Model

Abstract

Taxi demand prediction is essential for intelligent transportation systems. Accurate prediction results help address the issue of supply–demand imbalances and enable more efficient traffic management. Significant advances have been made in traffic demand prediction, particularly through the use of deep learning models. However, these models heavily rely on a large amount of data. Data scarcity remains a significant challenge because of high acquisition and storage costs, as well as data sparsity in certain locations and times. Thus, this study proposes a novel taxi demand prediction model that leverages the large language model GPT-2 to capture complex spatio-temporal dependencies. By integrating spatial correlations through a graph attention network and incorporating temporal dependencies at multiple scales, the proposed spatio-temporal taxi demand prediction large model (STTDP-LM) is capable of achieving accurate prediction with limited training data. Extensive experiments validate its effectiveness across two districts in Xi’an. Compared to the baseline method, the STTDP-LM reduces the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by an average of 12.25%, 12.55%, and 18.33%, respectively, across the two districts. When trained with only 1% of the data, the model still shows significant improvement, with average reductions of 33.83%, 34.12%, and 17.03% in the RMSE, MAE, and MAPE, respectively. The prediction accuracy of the model is more prominent in multi-step prediction with a total duration of 60 min. In summary, this study offers a promising solution for taxi demand prediction with limited historical data, providing a valuable insight for real-world applications in intelligent transportation systems.