Utilizing smartphone sensor data from crowdsen-sing (CS) campaigns for transportation planning (TP) requires highly reliable transport mode recognition. To address this, we present our RNN-based AI model MovDeep, which works on GPS, accelerometer, magnetometer and gyroscope data. It was trained on 92 hours of labeled data. MovDeep predicts six transportation modes (TM) on one second time windows. A novel postprocessing further improves the prediction results. We present a validation methodology (VM), which simulates unknown context, to get a more realistic estimation of the real-world performance (RWP). We explain why existing work shows overestimated prediction qualities, when they would be used on CS data and why their results are not comparable with each other. With the introduced VM, MovDeep still achieves 99.3 % $F_{1}$ -Score on six TM. We confirm the very good RWP for our model on unknown context with the Sussex-Huawei Locomotion data set. For future model comparison, both publicly available data sets can be used with our VM. In the end, we compare MovDeep to a deterministic approach as a baseline for an average performing model (82 - 88 % RWP Recall) on a CS data set of 540 k tracks, to show the significant negative impact of even small prediction errors on TP.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    AI-Based Transport Mode Recognition for Transportation Planning Utilizing Smartphone Sensor Data From Crowdsensing Campaigns


    Beteiligte:
    Grubitzsch, Philipp (Autor:in) / Werner, Elias (Autor:in) / Matusek, Daniel (Autor:in) / Stojanov, Viktor (Autor:in) / Hahnel, Markus (Autor:in)


    Erscheinungsdatum :

    2021-09-19


    Format / Umfang :

    359527 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Privacy-Preserved Mobile Crowdsensing for Intelligent Transportation Systems

    Miao, Qinyang / Lin, Hui / Hu, Jia et al. | Springer Verlag | 2021


    Individual trip mode recognition based on smartphone GPS positioning data

    Zhang, Xiaolong | British Library Conference Proceedings | 2022


    A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data

    Liang, Xiaoyuan / Zhang, Yuchuan / Wang, Guiling et al. | IEEE | 2020


    Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation

    Wu, Wansen / Yang, Weiyi / Li, Juanjuan et al. | IEEE | 2024