PDHFormer: Progressive Dual-Head Transformer for Behavioral Choice Prediction

Many applications require joint prediction of interdependent behavioral choices, yet existing models often treat each choice independently (e.g., through parallel prediction heads), overlooking the influence of one on the other. In this work, we propose Progressive Dual-Head Transformer (PDHFormer), a novel framework that performs two-step prediction: the model first estimates one choice and then conditions the second on this upstream estimate through an explicit head-to-head pathway. A shared encoder captures the common structure of two prediction tasks, while the dual-head module explicitly reflects cross-choice dependence. A gated residual mechanism integrated into the embedding layer and the dual-head module further improves the training stability and the prediction performance. Extensive experiments on real-world urban mobility, manufacturing, and food delivery application domains demonstrate that PDHFormer consistently outperforms state-of-the-art machine learning models, deep tabular models, as well as parallel-head Transformer variants across multiple metrics. Moreover, our ablation study confirms that both the proposed progressive dual-head and gated residual mechanism are key contributors to the observed gains in different prediction tasks.