Vehicle Delay Prediction at Urban Roundabouts: Comparing Historical, Operational, and Demand-Based Features

Accurate short-term traffic delay prediction is essential for effective intersection management and real-time traffic control. Although deep learning models have shown strong predictive capabilities in traffic forecasting, the influence of input feature configuration on prediction performance remains insufficiently understood. This study investigates how different feature groups affect short-term delay prediction at an urban roundabout using high-resolution, approach-level traffic data collected at one-minute intervals. Five feature scenarios are evaluated, ranging from temporal indicators only (S0) to a comprehensive feature set combining historical delay, operational traffic indicators, demand measurements, and temporal context (S4). Two recurrent neural network architectures, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are examined under two forecasting horizons (1-min and 5-min ahead). To ensure robustness, each configuration is trained through repeated runs and evaluated using statistical significance analysis. Results show that the temporal-only baseline produces the largest prediction errors (MAE ≈ 22.5 s), while scenarios incorporating operational traffic indicators significantly improve prediction accuracy. The full feature configuration (S4) achieves the best performance for the 1-min horizon, reaching MAE values of 17.24 s and 17.22 s for GRU and LSTM, respectively. For the 5-min horizon, prediction errors increase and performance differences between feature scenarios become smaller. Additional experiments across multiple approaches confirm the general consistency of the proposed framework, while hyperparameter sensitivity analysis indicates limited dependence on model capacity. Overall, the findings highlight the importance of operational traffic indicators—particularly queue dynamics and stop patterns—for reliable short-term delay forecasting and provide practical guidance for designing efficient real-time traffic prediction systems.