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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-time demand forecasting for an urban delivery platform


    Contributors:


    Publication date :

    2020-10-31




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Dynamic forecasting of urban shopping travel demand

    Oppenheim, Norbert | Elsevier | 1985


    Real-Time Traffic Volatility Forecasting in Urban Arterial Networks

    Tsekeris, Theodore / Stathopoulos, Antony | Transportation Research Record | 2006