research-article

Service Time Prediction for Delivery Tasks via Spatial Meta-Learning

Authors:

Yu ZhengAuthors Info & Claims

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

Pages 3829 - 3837

https://doi.org/10.1145/3534678.3539027

Published: 14 August 2022 Publication History

Abstract

Service time is a part of time cost in the last-mile delivery, which is the time spent on delivering parcels at a certain location. Predicting the service time is fundamental for many downstream logistics applications, e.g., route planning with time windows, courier workload balancing and delivery time prediction. Nevertheless, it is non-trivial given the complex delivery circumstances, location heterogeneity, and skewed observations in space. The existing solution trains a supervised model based on aggregated features extracted from parcels to deliver, which cannot handle above challenges well. In this paper, we propose MetaSTP, a meta-learning based neural network model to predict the service time. MetaSTP treats the service time prediction at each location as a learning task, leverages a Transformer-based representation layer to encode the complex delivery circumstances, and devises a model-based meta-learning method enhanced by location prior knowledge to reserve the uniqueness of each location and handle the imbalanced distribution issue. Experiments show MetaSTP outperforms baselines by at least 9.5% and 7.6% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP is deployed and used internally in JD Logistics.

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Yu FZhang GWang HJin DLi Y(2024)Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration FrameworkACM Transactions on Intelligent Systems and Technology10.1145/366348415:5(1-25)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3663484
Hong ZWang HDing YWang GHe TZhang D(2024)SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery BehaviorProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595968:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659596
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Index Terms

Service Time Prediction for Delivery Tasks via Spatial Meta-Learning
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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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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Published: 14 August 2022

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National Key Research and Development Program of China
Ministry of Education, Singapore
National Natural Science Foundation of China

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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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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Yu FZhang GWang HJin DLi Y(2024)Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration FrameworkACM Transactions on Intelligent Systems and Technology10.1145/366348415:5(1-25)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3663484
Hong ZWang HDing YWang GHe TZhang D(2024)SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery BehaviorProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595968:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659596
Wu LWen HHu HMao XXia YShan EZheng JLou JLiang YYang LZimmermann RLin YWan HBaeza-Yates RBonchi F(2024)LaDe: The First Comprehensive Last-mile Express Dataset from IndustryProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671548(5991-6002)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671548
Ma ZTu ZChen XZhang YXia DZhou GChen YZheng YGong JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645644(3064-3075)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645644
Hong ZWang GLyu WGuo BDing YWang HWang SLiu YZhang D(2024)Nationwide Behavior-Aware Coordinates Mining From Uncertain Delivery EventsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341156236:11(6681-6698)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3411562
Wang STang SRuan SLong CLiang YLi QYuan ZBao JZheng Y(2024)Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00318(4167-4179)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00318
Wei QLu YDu DCai HLai PWang C(2024)PLSRP: prompt learning for send–receive path predictionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02387-xOnline publication date: 26-Sep-2024
https://doi.org/10.1007/s13042-024-02387-x
Lyu WWang HSong YLiu YHe TZhang DElkind E(2023)A prediction-and-scheduling framework for efficient order transfer in logisticsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/680(6130-6137)Online publication date: 19-Aug-2023
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Hong ZCao DWang HWang GHe TZhang DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AutoBuild: Automatic Community Building Labeling for Last-mile DeliveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614658(4623-4630)Online publication date: 21-Oct-2023
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