Time versus Space: A Systematic Empirical Study of Information Allocation in Large-Scale Traffic Forecasting

Spatio-temporal traffic forecasting has largely focused on increasingly sophisticated graph-based models, implicitly assuming that spatial information is the primary source of predictive power. We revisit this assumption by asking a fundamental question: given a fixed observation budget, is forecasting accuracy improved more by allocating observations to temporal depth or spatial breadth? We answer this through a systematic empirical study of this trade-off in large-scale traffic forecasting, showing that predictive performance is determined primarily by whether the input covers one complete daily cycle of the target sensor, while additional spatial information provides only marginal benefit. We further explain this finding by showing that, under typical benchmark resolutions, congestion propagation between neighbouring sensors is faster than the observation interval, leaving little exploitable lead–lag information for graph-based models. On a high-resolution freeway corridor where propagation is resolved, physically relevant neighbourhoods instead become indispensable as traffic approaches capacity. These insights naturally lead to a simple graph-free temporal-only forecasting recipe that combines long-context temporal modelling with lightweight adaptive gating for atypical conditions, achieving state-of-the-art average accuracy on all four LargeST subsets. The principle is established with a lightweight model trained from scratch and transfers to an off-the-shelf pretrained time-series foundation model with only lightweight adaptation. Our findings indicate that predictive information is far more effectively captured through temporal depth than additional spatial observations, providing a new perspective on how spatio-temporal models should allocate their capacity.