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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    Title :

    Synthetic Population Generation for Autonomous Vehicle Demand Forecasting


    Contributors:


    Publication date :

    2024-10-29


    Size :

    1287267 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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