Identifying vehicle trajectories from video surveillance is an essential task in modern traffic management systems to prevent accidents and reduce congestion. Detecting cluster structures in trajectory paths is crucial for distinguishing between normal and abnormal vehicle behaviours. Traditional visual clustering approaches, such as Visual Assessment of cluster Tendency (VAT) and improved VAT (iVAT), are effective for visualizing cluster patterns but are limited in capturing complex motion characteristics due to inadequate feature representation. To address this limitation, this paper proposes a novel Deep Motion-Aware Trajectory iVAT(DMAT-iVAT) framework that integrates motion-enhanced trajectory modelling with deep learning-based feature extraction. The proposed method augments trajectory data with velocity and acceleration information to capture rich spatio-temporal dynamics, followed by representation learning using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The learned embeddings are then analyzed using iVAT to reveal cluster tendencies more effectively. Experimental results on benchmark video trajectory datasets demonstrate that the proposed DMAT-iVAT framework significantly improves cluster structure visualization and discrimination compared to conventional VAT-based methods.