Alexander Hess
a440752c4e
- streamline first half - add "on a grid" - add note on predictive routing applications - mention managerial findings and winning forecasting method in second half
47 lines
No EOL
1.5 KiB
TeX
47 lines
No EOL
1.5 KiB
TeX
\begin{frontmatter}
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\journal{Transportation Research Part E}
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\title{Real-time Demand Forecasting for an Urban Delivery Platform}
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\author[WHU]{Alexander Hess\fnref{emails}}
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\author[WHU]{Stefan Spinler\fnref{emails}}
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\author[MIT]{Matthias Winkenbach\fnref{emails}}
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\address[WHU]{
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WHU - Otto Beisheim School of Management,
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Burgplatz 2, 56179 Vallendar, Germany
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}
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\address[MIT]{
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Massachusetts Institute of Technology,
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77 Massachusetts Avenue, Cambridge, MA 02139, United States
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}
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\fntext[email]{
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Emails:
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alexander.hess@whu.edu,
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stefan.spinler@whu.edu,
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mwinkenb@mit.edu
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}
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\begin{abstract}
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Meal delivery platforms like Uber Eats shape the landscape in cities around the world.
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This paper addresses forecasting demand on a grid into the short-term future,
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enabling, for example, predictive routing applications.
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We propose an approach incorporating
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both classical forecasting and machine learning methods
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and adapt model evaluation and selection to typical demand:
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intermittent with a double-seasonal pattern.
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An empirical study shows that
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an exponential smoothing based method trained on past demand data alone
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achieves optimal accuracy,
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if at least two months are on record.
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With only a shorter demand history,
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machine learning methods and real-time data may improve prediction.
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\end{abstract}
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\begin{keyword}
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demand forecasting \sep
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intermittent demand \sep
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machine learning \sep
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urban delivery platform
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\end{keyword}
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\end{frontmatter} |