\begin{frontmatter}

\journal{Transportation Research Part E}
\title{Real-time demand forecasting for an urban delivery platform}

\author[WHU]{Alexander Hess\fnref{emails}\fnref{corresponding}}
\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
}

\fntext[corresponding]{
The corresponding author is Alexander Hess.
Use the provided email.
}

\begin{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.
\end{abstract}

\begin{keyword}
demand forecasting \sep
intermittent demand \sep
machine learning \sep
urban delivery platform
\end{keyword}

\end{frontmatter}