47 lines
1.4 KiB
TeX
47 lines
1.4 KiB
TeX
|
\begin{frontmatter}
|
||
|
|
|
||
|
|
\journal{Transportation Research Part E}
|
||
|
|
\title{Real-time Demand Forecasting for an Urban Delivery Platform}
|
||
|
|
|
||
|
|
\author[WHU]{Alexander Hess\fnref{emails}}
|
||
|
|
\author[WHU]{Stefan Spinler\fnref{emails}}
|
||
|
|
\author[MIT]{Matthias Winkenbach\fnref{emails}}
|
||
|
|
\address[WHU]{
|
||
|
|
WHU - Otto Beisheim School of Management,
|
||
|
|
Burgplatz 2, 56179 Vallendar, Germany
|
||
|
|
}
|
||
|
|
\address[MIT]{
|
||
|
|
Massachusetts Institute of Technology,
|
||
|
|
77 Massachusetts Avenue, Cambridge, MA 02139, United States
|
||
|
|
}
|
||
|
|
\fntext[email]{
|
||
|
|
Emails:
|
||
|
|
alexander.hess@whu.edu,
|
||
|
|
stefan.spinler@whu.edu,
|
||
|
|
mwinkenb@mit.edu
|
||
|
|
}
|
||
|
|
|
||
|
|
\begin{abstract}
|
||
|
|
Meal delivery platforms like Uber Eats shape the landscape in cities around the world.
|
||
|
|
This paper addresses forecasting demand into the short-term future.
|
||
|
|
We propose an approach incorporating
|
||
|
|
both classical forecasting
|
||
|
|
and machine learning methods.
|
||
|
|
Model evaluation and selection is adapted to demand typical for such a platform
|
||
|
|
(i.e., intermittent with a double-seasonal pattern).
|
||
|
|
The results of an empirical study with a European meal delivery service show
|
||
|
|
that machine learning models become competitive
|
||
|
|
once the average daily demand passes a threshold.
|
||
|
|
As a main contribution, the paper explains
|
||
|
|
how a forecasting system must be set up
|
||
|
|
to enable predictive routing.
|
||
|
|
\end{abstract}
|
||
|
|
|
||
|
|
\begin{keyword}
|
||
|
|
demand forecasting \sep
|
||
|
|
intermittent demand \sep
|
||
|
|
machine learning \sep
|
||
|
|
urban delivery platform
|
||
|
|
\end{keyword}
|
||
|
|
|
||
|
|
\end{frontmatter}
|