Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

The prediction of expressway vehicle trajectories is a crucial aspect in the development of intelligent expressways. This paper proposes a novel approach, namely the W-GRU-Attention (WGA) model, which utilizes ETC transaction data to predict trajectory selection based on historical traffic paths and previous passed gantry information. In this study, we apply the concept of word embedding models to extract contextual semantics from the historical trajectories on expressways. Additionally, we introduce an average pooling technique for converting the historical vehicle trajectory into a fixed-length Historical Trajectory Vector (HTV), enabling us to capture dependency relationships within experience paths. By combining proximity gantry vectors during transit, we accurately predict the next gantry location. Finally, our proposed method is evaluated using a real-world expressway ETC dataset. It achieves an impressive accuracy rate of 96.14% in capturing the relationship between historical trajectories and adjacent gantries, surpassing other models in path prediction.