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An integrated framework for accurate trajectory prediction based on deep learning

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Abstract

Trajectory prediction for moving objects is a critical task for intelligent transportation with numerous applications, such as route planning, traffic management, congestion alleviation, etc. In this paper, we propose a novel framework that integrates sequence modeling, trajectory clustering and topology extraction to improve the accuracy of trajectory prediction. By incorporating self-attention for sequence modeling, we are able to effectively capture the temporal dependencies in trajectory data. Additionally, by taking into account the clustering information via a variational auto-encoder and the topological information based on a graphical neural network (GNN), we can further improve the accuracy of trajectory prediction. Furthermore, integrating a GNN facilitates our framework to handle diverse characteristics of road networks, such as road distance and traffic status, thereby making the proposed approach adaptive to different practical scenarios. As demonstrated by the experimental results on two publicly available datasets, our proposed method improves the accuracies by up to 0.5% and 3.8% for 1-step and 15-step predictions respectively, compared to the state-of-the-art method.

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Data Availability

The datasets used in our study are available using the following links: Porto dataset-https://www.kaggle.com/datasets/crailtap/taxi-trajectory. Chengdu dataset-https://pan.baidu.com/s/1OeNs36fZHEon2yNA2bhs9A with access code hqen.

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Authors and Affiliations

  1. Data Science Research Center, Duke Kunshan University, 215316, Kunshan, P. R. China

    Shuo Zhao, Zhaozhi Li, Zikun Zhu, Charles Chang, Xin Li & Ying-Chi Chen

  2. Shanghai ConvertLab Co. Ltd., 201100, Shanghai, P. R. China

    Bo Yang

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  1. Shuo Zhao
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  2. Zhaozhi Li
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  5. Xin Li
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Contributions

Shuo Zhao: Methodology, Software, Writing-Original draft. Zhaozhi Li: Software, Writing-Review Zikun Zhu: Software, Writing-Review. Charles Chang: Conceptualization, Methodology, Writing-Reveiw. Xin Li: Conceptualization, Methodology, Writing-Original draft, Review & Editing. Ying-Chi Chen: Conceptualization, Methodology, Writing-Review. Bo Yang: Conceptualization, Methodology, Writing-Review.

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Correspondence to Xin Li.

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Zhao, S., Li, Z., Zhu, Z. et al. An integrated framework for accurate trajectory prediction based on deep learning. Appl Intell 54, 10161–10175 (2024). https://doi.org/10.1007/s10489-024-05724-3

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