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https://doi.org/10.7307/ptt.v36i3.420

Trajectory Prediction of Port Container Trucks Based on DeepPBM-Attention

Haixiong YE ; College of Engineering Science and Technology, Shanghai Ocean University
Kairong LUAN ; College of Engineering Science and Technology, Shanghai Ocean University
Mei YANG ; Shanghai International Port (Group) Co., Ltd.; Academy for Engineering and Technology, Fudan University
Xiliang ZHANG ; School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University
Yue ZHOU ; College of Engineering Science and Technology, Shanghai Ocean University


Puni tekst: engleski pdf 3.707 Kb

str. 525-543

preuzimanja: 101

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Sažetak

Existing tracking algorithms mostly rely on model-driven approaches, which can be prone to inaccuracies due to unpredictable human behaviours. This article aims to address the issue of transient errors in tracking port container trucks (PCTrucks) when encountering obstructions. A data-driven algorithm for predicting vehicle trajectories is proposed in this study. The approach involves preprocessing an extensive dataset of GPS information, training a DeepLSTM-Attention model, and integrating the proposed model with the population-based training (PBT) algorithm to optimise network hyperparameters. The objective is to enhance the accuracy of predicting trajectories for vehicles moving horizontally. The trajectory data used are collected from real-world port operations. This research is conducted across nine trajectory segments and benchmarked against traditional approaches like Kalman filtering, machine learning techniques such as support vector regression (SVR) and standard long short-term memory (LSTM) networks. The results demonstrate that the proposed prediction method, that is, DeepPBM-Attention, outperforms other techniques in several evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), F1 score and trajectory reconstruction error (TRE). Compared to LSTM networks, the performance of DeepPBM-Attention is improved by approximately 40%. The proposed data-driven trajectory prediction algorithm exhibits high accuracy and practicality, which can effectively be applied to the positioning prediction of horizontally moving vehicles in port environments.

Ključne riječi

port container trucks; trajectory prediction; population-based training; deep long short-term memory

Hrčak ID:

318725

URI

https://hrcak.srce.hr/318725

Datum izdavanja:

20.6.2024.

Posjeta: 312 *