Robustness of Spatio-Temporal Graph Neural Networks for Traffic Forecasting Under Realistic Sensor Degradation
Short-term traffic forecasting plays an important role in intelligent transportation systems, as applications such as route guidance, adaptive traffic signal control, emergency response, congestion mitigation, and logistics planning depend on accurate estimates of future traffic states. Recent spatio-temporal graph neural network models, particularly Diffusion Convolutional Recurrent Neural Network (DCRNN) and Spatio-Temporal Graph Convolutional Network (STGCN), have improved traffic prediction by representing road sensors as graph nodes and jointly learning spatial and temporal relationships. However, strong performance on clean benchmark datasets does not always guarantee reliability in real-world conditions, where sensor reading may be missing, noisy, or unavailable due to hardware faults or communication issues. This paper provides a robust focused comparative evaluation of seven traffic forecasting approaches: Persistence, Historical Average, ARIMA, Random Forest, LSTM, STGCN, and DCRNN. Experiments are conducted on the METR-LA and PEMS-BAY speed datasets using 5-minute data intervals, a 12-step historical input sequence, and a 12-step forecasting horizon. Under clean-data conditions, DCRNN achieves the best overall MAE on both datasets, with 3.548 mph on METR-LA and 1.905 mph on PEMS-BAY. However, when 40% random missing input corruption is introduced, STGCN shows greater robustness than DCRNN; for METR-LA, STGCN’s MAE increases by 27.0%, while DCRNN’s MAE increases by 34.5%. Under complete sensor-failure conditions, the model ranking changes further, with the LSTM baseline showing greater stability than both graph-based models on METR-LA. Graph ablation analysis also indicates that temporal modeling accounts for most of the forecast improvement, while the evaluated graph topology provides only limited additional benefits. These findings suggest that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.