Traffic incidents often lead to the closure of lanes, causing a reduction in road capacity. To handle such situations, Intelligent Transport Systems (ITS) are commonly employed to maximize the utilization of the remaining capacity. By leveraging data mining and deep learning techniques, our objective is to anticipate congestion patterns during such incidents and develop a real-time guidance system to aid drivers. To accomplish this, we build a Long Short-Term Memory neural network-based prediction model which incorporates up-to-date traffic speed-flow data and a range of spatiotemporal features as inputs and predicts the queue length of the incidents. Moreover, the model continuously predicts the queue length throughout the duration of the incidents, capturing the temporal dynamics of the situation. Initially, our proposed model exhibits an average error of 52.54%, which improves to 18.5% over the course of one hour of prediction.