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Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors

Authors:

Zhongxiang Sun,

Lixia WuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 3

Article No.: 50, Pages 1 - 22

https://doi.org/10.1145/3582561

Published: 13 April 2023 Publication History

Abstract

In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier’s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems alleviate customers’ waiting anxiety and improve their experience. We identify three main challenges of this problem. First, unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. Second, an intuitive idea for solving ETPA is to predict the pick-up route and then the time in two stages. However, it is difficult to accurately and efficiently predict couriers’ future routes in the route prediction step since their behaviors are affected by multiple complex factors. Third, furthermore, in the time prediction step, the requirement for providing a courier’s all unpicked-up packages’ ETPA at once in real time makes the problem even more challenging. To tackle the preceding challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models.

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Wang SYang QRuan SLong CYuan YLi QYuan ZBao JZheng Y(2025)Spatial Meta Learning With Comprehensive Prior Knowledge Injection for Service Time PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351258237:2(936-950)Online publication date: 1-Feb-2025
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Index Terms

Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 3

June 2023

451 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3587032

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 13 April 2023

Online AM: 08 February 2023

Accepted: 27 December 2022

Revised: 09 December 2022

Received: 05 December 2021

Published in TIST Volume 14, Issue 3

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Wang SYang QRuan SLong CYuan YLi QYuan ZBao JZheng Y(2025)Spatial Meta Learning With Comprehensive Prior Knowledge Injection for Service Time PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351258237:2(936-950)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3512582
Zhang XCao MGong YWu XDong XGuo YZhao LZhang C(2025)Enhancing urban flow prediction via mutual reinforcement with multi-scale regional informationNeural Networks10.1016/j.neunet.2024.106900182(106900)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106900
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
Yi JYan HWang HYuan JLi Y(2024)RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate PredictionACM Transactions on Intelligent Systems and Technology10.1145/3690649Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690649
Yi JYan HWang HYuan JLi Y(2024)Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics ServicesProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691266(432-443)Online publication date: 29-Oct-2024
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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
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Wen HLin YWu LMao XCai THou YGuo SLiang YJin GZhao YZimmermann RYe JWan H(2024)A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and ProspectsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344130936:12(7516-7535)Online publication date: 1-Dec-2024
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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
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Wang SWang ZRuan SHan HXiong KYuan HYuan ZLi GBao JZheng Y(2024)DelvMap: Completing Residential Roads in Maps Based on Couriers’ Trajectories and Satellite ImageryIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.336583362(1-14)Online publication date: 2024
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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
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