How Much Structural Abstraction Does Traffic Forecasting Need? A Benchmark for Controllable Multimodal Representations

Traffic forecasting combines temporal, visual, and graph representations, yet it remains unclear how much structural abstraction should be provided to a model rather than inferred from raw observations. Benchmarks make this question difficult to isolate because representation type, graph construction, and model architecture are usually varied together. We introduce GB-Traffic, a representation-centric benchmark that aligns temporal signals, multiscale visual encodings, and functional temporal graphs while controlling structural abstraction from coarse to fine. All representations are constructed from historical observations, allowing abstraction level to be studied without changing the prediction target. The benchmark covers six datasets spanning highway, urban-road, bus rapid transit, and metro systems under a unified forecasting protocol and evaluates granularity controllability, functional-edge verification, multimodal complementarity, structural utility across backbones, cross-granularity transfer, and zero-shot cross-domain transfer. Results show that increasing structural resolution is not uniformly beneficial. Intermediate abstraction provides a consistent balance between local detail and structural coherence, while functional temporal graphs provide complementary information beyond physical connectivity across settings. Cross-domain results show that transferability depends on the representation regime. These observations establish structural abstraction as an explicit experimental variable in spatio-temporal forecasting rather than a fixed preprocessing decision. Code, representations, and evaluation resources are available at the anonymous repository: https://anonymous.4open.science/r/GB-Traffic-5FCB.