research-article

Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service

Authors:

Chengliang Gao,

Zhizhao SunAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2924 - 2934

https://doi.org/10.1145/3534678.3539035

Published: 14 August 2022 Publication History

Abstract

On-demand food delivery service has widely served people's daily demands worldwide, e.g., customers place over 40 million online orders in Meituan food delivery platform per day in Q3 of 2021. Predicting the food preparation time (FPT) of each order accurately is very significant for the courier and customer experience over the platform. However, there are two challenges, namely incomplete label and huge uncertainty in FPT data, to make the prediction of FPT in practice. In this paper, we apply probabilistic forecasting to FPT for the first time and propose a non-parametric method based on deep learning. Apart from the data with precise label of FPT, we make full use of the lower/upper bound of orders without precise label, during feature extraction and model construction. A number of categories of meaningful features are extracted based on the detailed data analysis to produce sharp probability distribution. For probabilistic forecasting, we propose S-QL and prove its relationship with S-CRPS for interval-censored data for the first time, which serves the quantile discretization of S-CRPS and optimization for the constructed neural network model. Extensive offline experiments over the large-scale real-world dataset, and online A/B test both demonstrate the effectiveness of our proposed method.

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Cited By

Yang JLau HWang H(2024)Optimization of customer service and driver dispatch areas for on-demand food deliveryTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104653165(104653)Online publication date: Aug-2024
https://doi.org/10.1016/j.trc.2024.104653
Zhao SKong MLi RHounye ASu RHou MCao C(2024)SCARNet: using convolution neural network to predict time series with time-varying varianceMultimedia Tools and Applications10.1007/s11042-024-19322-5Online publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19322-5
Yu XLan AMao H(2023)Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTMSystems10.3390/systems1110048511:10(485)Online publication date: 22-Sep-2023
https://doi.org/10.3390/systems11100485
Show More Cited By

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Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service

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Published In

cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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Publication History

Published: 14 August 2022

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NSFC
National Key Research and Development Program
Meituan

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Yang JLau HWang H(2024)Optimization of customer service and driver dispatch areas for on-demand food deliveryTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104653165(104653)Online publication date: Aug-2024
https://doi.org/10.1016/j.trc.2024.104653
Zhao SKong MLi RHounye ASu RHou MCao C(2024)SCARNet: using convolution neural network to predict time series with time-varying varianceMultimedia Tools and Applications10.1007/s11042-024-19322-5Online publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19322-5
Yu XLan AMao H(2023)Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTMSystems10.3390/systems1110048511:10(485)Online publication date: 22-Sep-2023
https://doi.org/10.3390/systems11100485
Zhang LZhou XZeng ZCao YXu YWang MWu XLiu YCui LShen Z(2023)Delivery Time Prediction Using Large-Scale Graph Structure Learning Based on Quantile Regression2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00261(3403-3416)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00261
Guo BZuo WWang SZhou XHe T(2023)Attention Enhanced Package Pick-Up Time Prediction via Heterogeneous Behavior ModelingAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0862-8_12(189-208)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-0862-8_12

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