Highlights Predicting demand of an urban meal delivery platform like Uber Eats into the near future. Implement a forecasting system for predictive routing. Predicting demand with machine learning based methods.

    Abstract Meal delivery platforms like Uber Eats shape the landscape in cities around the world. This paper addresses forecasting demand on a grid into the short-term future, enabling, for example, predictive routing applications. We propose an approach incorporating both classical forecasting and machine learning methods and adapt model evaluation and selection to typical demand: intermittent with a double-seasonal pattern. An empirical study shows that an exponential smoothing based method trained on past demand data alone achieves optimal accuracy, if at least two months are on record. With a more limited demand history, machine learning is shown to yield more accurate prediction results than classical methods.


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    Titel :

    Real-time demand forecasting for an urban delivery platform


    Beteiligte:


    Erscheinungsdatum :

    2020-10-31




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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