Seeing the Unseen Nodes: Zero-Shot and Few-Shot Traffic Flow Prediction via Semantic Prototype-based Learning

Traffic flow forecasting is a fundamental task in intelligent transportation systems. However, most existing methods assume a fixed set of sensors and operate under a transductive setting, limiting their ability to handle continuously expanding traffic networks. While previous studies have explored traffic expansion forecasting, they typically rely on sufficient observations from newly deployed sensors. In contrast, we study a more challenging setting in which new sensors come with only a few or even no historical observations, referred to as few-shot and zero-shot Traffic Expansion Flow Forecasting (TEFF). To tackle this problem, we propose a Semantic-driven Prototype-based Spatio-Temporal framework (SemProtoST). Specifically, we first introduce a metadata-driven node representation to enable inductive generalization to unseen nodes. We then develop dual spatio-temporal pattern prototypes, where node metadata and temporal features query shared prototypes via a cross-attention mechanism to model traffic patterns. Furthermore, we incorporate a semantic consistency constraint to enhance generalization to unseen nodes. To support few-shot adaptation, we design a knowledge distillation strategy that preserves performance on seen nodes while enabling effective adaptation to unseen nodes. Extensive experiments on four real-world datasets demonstrate that SemProtoST achieves average improvements of 3.58% and 14.22% on unseen nodes in few-shot and zero-shot settings, respectively. Our code is available at https://anonymous.4open.science/r/SemProtoST.