Online vehicle-for-hire service firms such as Ola, Uber, Gett, Lyft, Hailo, Didi, and GrabCab rely on driver recommendations. Several factors should be considered when suggesting a driver for the trip, including ride cost, driver-vehicle-passenger safety, reliability, robustness, and comfort. Multi-task learning has become more widespread in recommender systems in recent years. Our study employs federated multi-objective multitask learning to develop a driver recommendation system that considers drivers’ cognitive-physical stress and driving behavior while respecting users’ privacy. We use publicly available datasets from UAH-DriveSet, HCI Lab, and PhysioNet for training, testing, and evaluating our system. Our main contribution is a unique framework for driver suggestion based on data-driven federated multi-task learning that achieves the best F-measure accuracy. Our proposed system outperforms baseline and state-of-the-art deep learning models. It detects driver stress and behavior with 95% and 96% F-measures, respectively.


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    Titel :

    Dr. MTL: Driver Recommendation using Federated Multi-Task Learning


    Beteiligte:
    Vyas, Jayant (Autor:in) / Bhumika (Autor:in) / Das, Debasis (Autor:in) / Chaudhury, Santanu (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1615895 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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