Multiple-Step Traffic Speed Forecasting Strategy for Winter Freeway Operations

Accurate and timely predictions of traffic conditions are required for congestion avoidance and route guidance in real-time freeway traffic operations. Special attention to winter operations is needed because prediction error could be amplified under severe weather conditions involving snow. This study employed a vehicle detection system to propose a speed prediction methodology that used the k-nearest neighbors algorithm. The speed prediction was further evaluated under different weather conditions with a road weather information system. Cross-comparisons of the mean absolute percentage error (MAPE) between three weather conditions (normal, light snow, and heavy snow) revealed that the MAPE tended to increase with increases in the forecasting time step (T) and snow intensity. The marginal MAPE over the time step was larger during heavy snow conditions than under normal and light snow conditions. These findings indicate that for winter freeway operations, the time step should be selected dynamically, depending on the weather conditions rather than with a static strategy for all conditions. To this end, this study proposes a framework to determine a dynamic forecasting T that is associated with weather conditions.