research-article

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.

References

[1]

Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi. 2019. Seq2Slate: Re-ranking and slate optimization with RNNs. In Proceedings of the Workshop on Negative Dependence in Machine Learning.

[2]

Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, and Sébastien Martin. 2019. Travel time estimation in the age of big data. Operations Research 67, 2 (2019), 498–515.

Digital Library

[3]

Chuanfa Chen, Yanyan Li, Changqing Yan, Honglei Dai, and Guolin Liu. 2015. A robust algorithm of multiquadric method based on an improved Huber loss function for interpolating remote-sensing-derived elevation data sets. Remote Sensing 7, 3 (2015), 3347–3371.

[4]

Chao Chen, Sen Yang, Yasha Wang, Bin Guo, and Daqing Zhang. 2020. CrowdExpress: A probabilistic framework for on-time crowdsourced package deliveries. IEEE Transactions on Big Data 8, 3 (2020), 827–842.

[5]

Chao Chen, Daqing Zhang, Xiaojuan Ma, Bin Guo, Leye Wang, Yasha Wang, and Edwin Sha. 2016. crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2016), 1478–1496.

[6]

Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724–1734.

[7]

Arthur Cruz de Araujo and Ali Etemad. 2021. End-to-end prediction of parcel delivery time with deep learning for smart-city applications. IEEE Internet of Things Journal 8, 23 (2021), 17043–17056.

[8]

Corrado De Fabritiis, Roberto Ragona, and Gaetano Valenti. 2008. Traffic estimation and prediction based on real time floating car data. In Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems. 197–203.

[9]

Kun Fu, Fanlin Meng, Jieping Ye, and Zheng Wang. 2020. CompactETA: A fast inference system for travel time prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 3337–3345.

Digital Library

[10]

Luca Maria Gambardella, Éric Taillard, and Giovanni Agazzi. 1999. MACS-VRPTW: A multiple colony system for vehicle routing problems with time windows. In New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover (Eds.). McGraw-Hill, London, UK, 63–76.

Digital Library

[11]

Chengliang Gao, Fan Zhang, Guanqun Wu, Qiwan Hu, Qiang Ru, Jinghua Hao, Renqing He, and Zhizhao Sun. 2021. A deep learning method for route and time prediction in food delivery service. In Proceedings of the 27th ACm SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’21). 2879–2889.

Digital Library

[12]

Felix A. Gers, Jürgen Schmidhuber, and Fred Cummins. 2000. Learning to forget: Continual prediction with LSTM. Neural Computation 12, 10 (2000), 2451–2471.

Digital Library

[13]

Rajeev Goel and Raman Maini. 2017. Vehicle routing problem and its solution methodologies: A survey. International Journal of Logistics Systems and Management 28, 4 (2017), 419–435.

[14]

Junliang Guo, Linli Xu, and Enhong Chen. 2020. Jointly masked sequence-to-sequence model for non-autoregressive neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 376–385.

[15]

Xuanli He, Gholamreza Haffari, and Mohammad Norouzi. 2018. Sequence to sequence mixture model for diverse machine translation. In Proceedings of the 2018 Conference on Natural Language Learning. 583–592.

[16]

Google OR-Tools. n.d. Vehicle Routing Problem. Retrieved February 17, 2023 from https://developers.google.com/optimization/routing/vrp.

[17]

Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen, and Hui Xiong. 2020. Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. In Proceedings of the 2020 IEEE 36th International Conference on Data Engineering. 1417–1428.

[18]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning. 448–456.

Digital Library

[19]

Erik Jenelius and Haris N. Koutsopoulos. 2013. Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B: Methodological 53 (2013), 64–81.

[20]

Ishan Jindal, Tony (Zhiwei) Qin, Xuewen Chen, Matthew Nokleby, and Jieping Ye. 2017. A unified neural network approach for estimating travel time and distance for a taxi trip. arXiv preprint arXiv:1710.04350 (2017).

[21]

Maurice G. Kendall. 1938. A new measure of rank correlation. Biometrika 30, 1-2 (1938), 81–93.

[22]

Wouter Kool, Herke van Hoof, and Max Welling. 2019. Attention, learn to solve routing problems! In Proceedings of the 7th International Conference on Learning Representations.

[23]

Gakuto Kurata, Bing Xiang, and Bowen Zhou. 2016. Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 521–526.

[24]

Haibing Li and Andrew Lim. 2003. Local search with annealing-like restarts to solve the VRPTW. European Journal of Operational Research 150, 1 (2003), 115–127.

[25]

Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, and Yan Liu. 2018. Multi-task representation learning for travel time estimation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1695–1704.

Digital Library

[26]

Duncan McFarlane, Vaggelis Giannikas, and Wenrong Lu. 2016. Intelligent logistics: Involving the customer. Computers in Industry 81 (2016), 105–115.

Digital Library

[27]

Graham Neubig. 2017. Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619 (2017).

[28]

Jing Qiu, Lei Du, Dongwen Zhang, Shen Su, and Zhihong Tian. 2019. Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Transactions on Industrial Informatics 16, 4 (2019), 2659–2666.

[29]

Suttinee Sawadsitang, Dusit Niyato, Kongrath Suankaewmanee, and Puay Siew Tan. 2019. Re-route package pickup and delivery planning with random demands. In Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[30]

Raffi Sevlian and Ram Rajagopal. 2010. Travel time estimation using floating car data. arXiv preprint arXiv:1012.4249 (2010).

[31]

Abdullah Aziz Sharfuddin, Md. Nafis Tihami, and Md. Saiful Islam. 2018. A deep recurrent neural network with BiLSTM model for sentiment classification. In Proceedings of the 2018 International Conference on Bangla Speech and Language Processing. 1–4.

[32]

Yaqiang Sun and Ailing Chen. 2021. Algorithm design for solving VRPTW problem in supermarket chain distribution. In Proceedings of the 2021 4th International Conference on Information Management and Management Science. 71–75.

Digital Library

[33]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14). 3104–3112.

[34]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30. 5998–6008.

[35]

Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Advances in Neural Information Processing Systems 28. 2692–2700.

[36]

Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. 2018. When will you arrive? Estimating travel time based on deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence.

[37]

Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2019. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 1–22.

Digital Library

[38]

Zheng Wang, Kun Fu, and Jieping Ye. 2018. Learning to estimate the travel time. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 858–866.

Digital Library

[39]

Fan Wu and Lixia Wu. 2019. DeepETA: A spatial-temporal sequential neural network model for estimating time of arrival in package delivery system. In Proceedings of the AAAI Conference on Artificial Intelligence. 774–781.

Digital Library

[40]

Neo Wu, Bradley Green, Xue Ben, and Shawn O’Banion. 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317 (2020).

[41]

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the 32nd International Conference on Machine Learning (ICML’15). 2048–2057.

[42]

Jin Yamanaka, Shigesumi Kuwashima, and Takio Kurita. 2017. Fast and accurate image super resolution by deep CNN with skip connection and network in network. In Proceedings of the International Conference on Neural Information Processing. 217–225.

[43]

Wenqiang Zhang, Diji Yang, Guohui Zhang, and Mitsuo Gen. 2020. Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW. Expert Systems with Applications 145 (2020), 113151.

Digital Library

[44]

Lin Zhu, Wei Yu, Kairong Zhou, Xing Wang, Wenxing Feng, Pengyu Wang, Ning Chen, and Pei Lee. 2020. Order fulfillment cycle time estimation for on-demand food delivery. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2571–2580.

Digital Library

Cited By

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
Show More Cited By

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

Recommendations

DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction
Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and ...
Modeling Spatial–Temporal Constraints and Spatial-Transfer Patterns for Couriers’ Package Pick-up Route Prediction
Couriers’ package pick-up route prediction is a fundamental task in the emerging intelligent logistics systems. It is beneficial for order dispatching and arrival-time estimation by leveraging the predicted routes to improve those downstream tasks. ...
Attention Enhanced Package Pick-Up Time Prediction via Heterogeneous Behavior Modeling
Algorithms and Architectures for Parallel Processing
Abstract
The logistics industry has developed rapidly with the popularity of online-to-offline businesses in recent years. First-mile package pick-up is one of the most critical and expensive parts of the whole logistics service chain, which is finished by ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Fundamental Research Funds for the Central Universities

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
525
Total Downloads

Downloads (Last 12 months)203
Downloads (Last 6 weeks)33

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
https://dl.acm.org/doi/10.1145/3678717.3691266
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
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 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
https://doi.org/10.1109/TGRS.2024.3365833
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
Alkazzi JOkumura K(2024)A Comprehensive Review on Leveraging Machine Learning for Multi-Agent Path FindingIEEE Access10.1109/ACCESS.2024.339230512(57390-57409)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3392305
Sentanoe WSaha SDiyanananda YManzoor ADasanayake BThyssens D(2024)Graph2RETA: Graph Neural Networks for Pick-up and Delivery Route Prediction and Arrival Time EstimationKI 2024: Advances in Artificial Intelligence10.1007/978-3-031-70893-0_17(232-245)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70893-0_17

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents