MASKED AUTOENCODER-BASED SPATIO-TEMPORAL GRAPH NEURAL NETWORK FOR ROAD TRAFFIC FLOW RECONSTRUCTION

Traffic sensor networks frequently suffer from missing data due to equipment failures and communication disruptions, degrading downstream intelligent transportation system (ITS) applications that depend on complete observations. We propose MAST-GNN, a Masked Autoencoder-based Spatio-Temporal Graph Neural Network that reconstructs missing traffic measurements through co-optimized spatial and temporal components tailored to the reconstruction objective. A two-layer graph convolutional network propagates information from observed to masked nodes along the road topology. A multi-scale temporal encoder captures short-term fluctuations, daily periodicity, and long-term trends through three parallel branches fused via a learnable concatenation-projection. Seven structured masking strategies, four spatial and three temporal, simulate realistic sensor outage patterns. We evaluate MAST-GNN on production traffic data from 95 signalized intersections in Beijing, at two granularities that share a single navigation-derived source, an intersection-level flow view and a direction level turn-movement view, together with two public highway benchmarks. At a 30% mask rate, MAST-GNN attains R2 = 0.954 at the intersection level and R2 = 0.912 at the direction level, exceeding the strongest baseline at both granularities. Three paired ablations localize the gains: masked self-supervision (R2 0.061→0.682), multi-scale temporal encoding (0.682→0.912), and graph propagation under cold start (0.886 versus −0.024 for non-parametric spatial interpolation). A cold-start study on twenty intersections with no training history exposes the complementary roles of the two information sources: temporal periodicity dominates under normal operation, whereas spatial topology becomes the sole recovery channel when temporal information fails. A mask-rate sweep quantifies this conditional value: the gain over the time-only baseline remains negligible under normal operation (−0.001 at 30% masking) and grows with temporal deprivation, reaching +0.224 at 90% masking; under cold start, graph propagation restores R2 = 0.886 versus −0.024 for non-parametric spatial interpolation (+0.910). On public benchmarks, MAST-GNN achieves R2 = 0.731 on METR-LA (207 highway sensors) and R2 = 0.886 on PEMS-BAY (325 Bay Area sensors), demonstrating robust transfer to real-world highway networks. Cross-dataset analysis reveals that model capacity, graph connectivity, and feature dimensionality interact multiplicatively. The hidden dimension must exceed a graph-complexity-dependent threshold before auxiliary features can contribute substantially to reconstruction accuracy.