The growing interest in Automated Mobility on Demand (AMoD) services in passenger transportation necessitates accurate forecasting for successful deployment. However, the paucity of real-world data is a significant challenge. In this study, we present a unique technique for developing a synthetic user population tailored to AMoD car services. We identify possible passengers using selection criteria such as age, gender, activity status, and income, and then utilize a multi-agent simulation tool to define passenger movements within the AMoD service and plan out daily journeys. Additionally, a spatiotemporal analysis reveals use patterns that are well captured by Machine Learning models such as Random Forest, XGBoost, Neural Networks (NN), and Linear Regression (LR). Finally, by estimating spatio-temporal demand for automated cars, our model gives critical insights into the ideal allocation of fleet resources, thereby advancing the progress of AMoD transportation systems.


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    Titel :

    Synthetic Population Generation for Autonomous Vehicle Demand Forecasting


    Beteiligte:
    Sahbani, Bouchra (Autor:in) / Benatia, Mohamed Amin (Autor:in) / Pallares, Gael (Autor:in) / Louis, Anne (Autor:in)


    Erscheinungsdatum :

    29.10.2024


    Format / Umfang :

    1287267 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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