Research: real-time demand forecasting for an urban delivery platform

In recent years, many meal delivery platform providers (e.g., Uber Eats, GrubHub, DoorDash, Deliveroo) with different kinds of
business models have entered the markets in cities around the world. Meal delivery platforms like Uber Eats shape the landscape in cities around the world.

A new 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.

This study gives rise to the following managerial implications. First, UDPs can implement readily available forecasting algorithms with limited effort. This, however, requires purposeful data collection and preparation by those companies, which, according to our study, is at least equally important as the selection of the forecasting algorithm, as becomes clear from investigating the impact of the length of the training horizon.
Second, the benefits of moving from manual forecasting to automated forecasting include being able to pursue a predictive routing strategy and demand-adjusted shift scheduling. At the time the case study data was collected, our industry partner did not conduct any forecasting; the only forecasting-related activities were the shift managers scheduling the shifts one week in advance manually in spreadsheets.

Selecting the right forecasting algorithm according to the framework proposed in this study becomes a prerequisite to the much needed operational improvements UDPs need to achieve in their quest for profitability. In general, many UDPs launched in recent years are venture capital-backed start-up companies that almost by definition do not have a strong grounding in operational excellence, and publications such as the ones by Uber are the exception rather than the rule. The paper shows that forecasting the next couple of hours can already be implemented within the first year of a UDP’s operations. Even if such forecasts could not be exploited by predictive routing (e.g., due to prolonged waiting times at restaurants), they would help to monitor the operations for exceptional events.

Additionally, shift planning may be automated saving as much as one shift manager per city. We emphasize that for the most part, our proposed forecasting system is calibrated automatically and no manual work by a data scientist is required. The only two parameters where assumptions need to be made are the pixel size and the time step. The results in our empirical study suggest that a pixel size of 1 km2 and a time step of one hour are ideal, which results in the optimal trade-off between signal strength and spatial-temporal resolution. Future research may explore adaptive grid-sizing depending on, for instance, demand density. 

Source: Alexander Hess, Stefan Spinler, Matthias Winkenbach, Real-time demand forecasting for an urban delivery platform,
Transportation Research Part E: Logistics and Transportation Review, Volume 145, 2021, 102147, ISSN 1366-5545,



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