Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

Shukun Lai (), Hongke Xu, Fumin Zou, Yongyu Luo, Zerong Hu and Huan Zhong
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Shukun Lai: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Hongke Xu: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Fumin Zou: Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
Yongyu Luo: Fujian Provincial Expressway Information Technology Co., Ltd., Fuzhou 350011, China
Zerong Hu: Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
Huan Zhong: Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China

Sustainability, 2024, vol. 16, issue 11, 1-24

Abstract: 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.

Keywords: trajectory prediction; historical path dependencies; intelligent transportation systems; expressway (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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