CrossCityBench: A Diagnostic Benchmark for Cross-City Spatio-Temporal Generalization

Cross-city spatio-temporal forecasting asks whether source-city information improves prediction when a target city provides limited observations and follows a different distribution. Existing evidence is difficult to compare because studies vary source-target pairs, target-data budgets, forecasting protocols, and reported metrics. CrossCityBench evaluates target-only learning, alignment, meta-learning, pre-training, knowledge distillation, and federated learning under matched target information and forecasting conditions. It first tests whether source access improves on the strongest target-only reference, then examines whether the recorded ordering persists across computational cost, missing-data sensitivity, city-pair sensitivity, Spatio-Temporal Pattern Bank (STPB) alignment, and federated collaboration cost. Under two core tasks and a matched three-day target-information protocol, D2MHyper and CrossST are the only evaluated transfer methods whose recorded average Mean Absolute Error (MAE) is below the strongest target-only value on both tasks. Accuracy and sensitivity produce different empirical orderings, so in-domain error alone is insufficient for selection. STPB is an optional prototype-alignment diagnostic, not an explanation of learned representations. The resulting guide ties each candidate to measured evidence and remains restricted to the evaluated traffic settings. CrossCityBench provides a falsifiable protocol for making transfer claims comparable and diagnostically traceable. Code, configurations, and data-processing resources are available at https://anonymous.4open.science/r/CrossCityBench-87CB.