Real-time demand forecasting for an urban delivery platform

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.