Real-time demand forecasting for an urban delivery platform

Alexander Hess, Stefan Spinler and Matthias Winkenbach

Transportation Research Part E: Logistics and Transportation Review, 2021, vol. 145, issue C

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.

Keywords: Demand forecasting; Intermittent demand; Machine learning; Urban delivery platform (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.tre.2020.102147

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