Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

The COVID-19 pandemic has caused a dramatic change in the demand composition of restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such as DoorDash, Grubhub, and Uber Eats—to a large extent. With massive amounts of data on customers, drivers, and merchants, OFD platforms can achieve higher efficiency with better strategic and operational decisions; these include dynamic pricing, order bundling and dispatching, and driver relocation. Some of these decisions, and especially proactive decisions in real time, rely on accurate and reliable short-term predictions of demand ranges or distributions. In this paper, we develop a Poisson-based distribution prediction (PDP) framework equipped with a double-hurdle mechanism to forecast the range and distribution of potential customer demand. Specifically, a multi-objective function is designed to learn the likelihood of zero demand and approximate true demand and label distribution. An uncertainty-based multi-task learning technique is further employed to dynamically assign weights to different objective functions. The proposed model, evaluated by numerical experiments based on a real-world dataset collected from an OFD platform in Singapore, is shown to outperform several benchmarks by achieving more reliable demand range forecasting.

Keywords

Poisson-based distribution prediction, On-demand food delivery services

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Intelligent Transportation Systems

Volume

24

Issue

12

First Page

14556

Last Page

14569

ISSN

1524-9050

Identifier

10.1109/TITS.2023.3297948

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TITS.2023.3297948

Download

Share

COinS