%0 Dataset
%T 
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/05a70777-4c4f-49e2-bf79-43bf2232bf12
%W NCDC
%A chen du xin
%K Traffic flow;spatio-temporal forecasting;game-environment modeling
%X This dataset comprises multivariate time-series data representing road occupancy rates—measured by roadside sensor nodes—as they evolve over time. The data captures the spatiotemporal correlations between different regions, road segments, or nodes within the traffic system, as well as the dynamic characteristics of traffic state evolution. Variables are named according to sensor ID (nodes 0–861), with columns representing spatial monitoring nodes and rows representing timestamps. Compared to similar datasets, this dataset features high dimensionality, complex spatial correlation structures, and significant periodicity. It is well-suited for evaluating the ability of spatiotemporal prediction models to learn dynamic spatial dependencies and temporal evolution patterns in complex traffic scenarios. Key applications include game-theoretic environment modeling, traffic state prediction, spatial dependency modeling, analysis of dynamic interaction patterns, and functional validation of human-machine collaborative solving platforms.