While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Preserving Privacy with Federated Learning in Route Choice Behavior Modeling


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Kweon, Yonghyeon (Autor:in) / Sun, Bingrong (Autor:in) / Park, B. Brian (Autor:in)


    Erscheinungsdatum :

    2021-05-05




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning

    Liu, Yi / Zhang, Shuyu / Zhang, Chenhan et al. | IEEE | 2020




    Modeling Route Choice Behavior with Stochastic Learning Automata

    Ozbay, Kaan / Datta, Aleek / Kachroo, Pushkin | Transportation Research Record | 2001


    Modeling Route Choice Behavior

    Prato, Carlo Giacomo / Bekhor, Shlomo | Transportation Research Record | 2007