<p>Hard Shoulder Blocked.</p>

Performance was assessed using the mean absolute error (MAE) and the mean absolute percentage error (MAPE). The Medium Gaussian support vector machine provided the most balanced overall performance, and prediction accuracy generally improved from 6 to 16 predictors, with little additional benefit from 21 predictors. Compared with classical linear regression, the Medium Gaussian support vector machine reduced MAE by 5.7%–9.7% and MAPE by 21.2%–31.9% across the four feature settings.

A sensitivity comparison showed substantially higher errors when incidents longer than 60 minutes were retained, reinforcing that the main findings apply to routine incidents. Interpretation analyses identified Response Time and severity-related variables as important predictors, while residual errors remained larger for longer and more complex incidents. The model is intended primarily for post-arrival updating and clearance-support decisions for routine freeway incidents, rather than prediction at the initial alarm stage.</p></div>