Transferable Spatio-Temporal Node Identities: Elevating Multilayer Perceptrons to Leading Cross-City Zero-Shot Traffic Forecasting

Spatio-temporal forecasting models are conventionally trained and deployed per city, and the learnable node embeddings that empower state-of-the-art in-domain predictors are bound to the training network, precluding zero-shot transfer to unseen cities. We propose Transferable Spatio-Temporal Node Identities (TransSTNI), a paradigm that reconstructs node identity as a function computed from deployment-time priors rather than retrieved from dataset-bound parameters. TransSTNI derives node identity from three transferable sources: the Graph-Conditioned Structural Identity (GSI) encodes each node's structural role from the spectral coordinates of its own topology via a permutation-equivariant encoder; the Window-Conditioned Temporal-State Identity (WTI) captures node-level temporal states through multi-reference statistics computed over the current history window; and the Road-Attribute Semantic Embedding (RASE) provides functional context from de-geolocated road attributes through a frozen text encoder. These representations condition a residual MLP shared across all nodes, whose parameter count is independent of network size. To prevent memorization of the source topology, source-domain training interleaves full-graph learning with episodic optimization on variable-size induced subgraphs. At deployment, all parameters remain frozen: GSI and RASE are computed once and cached, and each window is predicted by a single forward pass without target-domain labels or adaptation. Experiments on LargeST demonstrate that TransSTNI achieves state-of-the-art zero-shot performance, while retaining the inference efficiency.