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- Publisher Website: 10.1109/TITS.2023.3297948
- Scopus: eid_2-s2.0-85166743765
- WOS: WOS:001047579800001
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Article: A Poisson-Based Distribution Learning Framework for Short-Term Prediction of Food Delivery Demand Ranges
Title | A Poisson-Based Distribution Learning Framework for Short-Term Prediction of Food Delivery Demand Ranges |
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Authors | |
Keywords | demand distribution label distribution learning on-demand food delivery Poisson distribution Short-term demand forecasting |
Issue Date | 1-Jul-2023 |
Publisher | IEEE |
Citation | IEEE Transactions on Intelligence Transportation Systems, 2023, v. 24, n. 12, p. 14556-14569 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/337920 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liang, J | - |
dc.contributor.author | Ke, J | - |
dc.contributor.author | Wang, H | - |
dc.contributor.author | Ye, H | - |
dc.contributor.author | Tang, J | - |
dc.date.accessioned | 2024-03-11T10:24:55Z | - |
dc.date.available | 2024-03-11T10:24:55Z | - |
dc.date.issued | 2023-07-01 | - |
dc.identifier.citation | IEEE Transactions on Intelligence Transportation Systems, 2023, v. 24, n. 12, p. 14556-14569 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/337920 | - |
dc.description.abstract | <p>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.</p> | - |
dc.language | eng | - |
dc.publisher | IEEE | - |
dc.relation.ispartof | IEEE Transactions on Intelligence Transportation Systems | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | demand distribution | - |
dc.subject | label distribution learning | - |
dc.subject | on-demand food delivery | - |
dc.subject | Poisson distribution | - |
dc.subject | Short-term demand forecasting | - |
dc.title | A Poisson-Based Distribution Learning Framework for Short-Term Prediction of Food Delivery Demand Ranges | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TITS.2023.3297948 | - |
dc.identifier.scopus | eid_2-s2.0-85166743765 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | 14556 | - |
dc.identifier.epage | 14569 | - |
dc.identifier.eissn | 1558-0016 | - |
dc.identifier.isi | WOS:001047579800001 | - |
dc.identifier.issnl | 1524-9050 | - |