Accurate prediction of vehicle speed with queued mixed traffic at signalized intersections, particularly under red light conditions, presents a significant challenge in traffic management. This paper proposes a novel hybrid model that combines a hierarchical neural network with Monte Carlo simulation to address this challenge. The hierarchical neural network predicts vehicle speed intervals at various levels of detail, while the Monte Carlo simulation refines these predictions. Results demonstrate effectiveness in improving the accuracy of minimum speed predictions in queued traffic environments.