GPS trajectories serve as a significant data source for travel mode identification along with the development of various GPS-enabled smart devices. However, such data directly integrate user private information, thus hindering users from sharing data with third parties. On the other hand, existing identification methods heavily depend on the respective manual travel mode annotations, whose production is economically inefficient and error-prone. In this paper, we propose a Semi-supervised Federated Learning (SSFL) framework that can accurately identify travel modes without using users’ raw trajectories data or relying on notable data labels. Specifically, we propose a new identification model named convolutional neural network-gated recurrent unit model in SSFL to accurately infer travel modes from GPS trajectories. Second, we design a pseudo-labeling method for the clients to set pseudo-labels on their local unlabeled dataset by using a small public dataset at the server. Furthermore, we adopt a grouping-based aggregation scheme and a data flipping augmentation scheme, which can boost the convergence and performance of the proposed framework. Comprehensive evaluations on a real-world dataset show that SSFL outperforms centralized semi-supervised baselines and is robust to the non-independent and identically distributed data commonly seen in practice.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories


    Contributors:
    Zhu, Yuanshao (author) / Liu, Yi (author) / Yu, James J. Q. (author) / Yuan, Xingliang (author)


    Publication date :

    2022-03-01


    Size :

    1592450 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    An Enhancing Semi-Supervised Federated Learning Framework for Internet of Vehicles

    Su, Xiangqing / Huo, Yan / Wang, Xiaoxuan et al. | IEEE | 2023


    Semi-supervised Federated Learning for Misbehavior Detection of BSMs in Vehicular Networks

    Huang, Jiaqi / Jiang, Yili / Gyawali, Sohan et al. | IEEE | 2024


    Identification of PWARX Systems Using Semi-supervised Learning

    Makihara, Fumiya / Kasajima, Takeo / Ogawa, Masatoshi | British Library Online Contents | 2019