Tabular Learning Based Traffic Event Prediction for Intelligent Social Transportation System

Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event prediction. With the tabular learning and model interpretability analysis, the importance of common traffic external factors toward traffic events is studied. The study has practical significance for regulating traffic planning and the development of the operational boundary for autonomous driving systems.