{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T07:28:22Z","timestamp":1774510102766,"version":"3.50.1"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers\u2019 fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/tsinghua-fib-lab\/Courier-DTRec\">https:\/\/github.com\/tsinghua-fib-lab\/Courier-DTRec<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3663484","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T12:27:15Z","timestamp":1718281635000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4207-3171","authenticated-orcid":false,"given":"Fudan","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University and TsingRoc, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0592-2285","authenticated-orcid":false,"given":"Guozhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University and TsingRoc, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-6389","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[{"name":"JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-5514","authenticated-orcid":false,"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/logistics5040084"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29821-0_10"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.23919\/SpliTech52315.2021.9566324"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/su12218769"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2991152"},{"issue":"6","key":"e_1_3_2_7_2","first-page":"1478","article-title":"Crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis","volume":"18","author":"Chen Chao","year":"2016","unstructured":"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. 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Digital twins: A critical discussion on their potential for supporting policy-making and planning in urban logistics. Sustainability 12, 24 (2020), 10623.","journal-title":"Sustainability"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467238"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3526087"},{"issue":"2","key":"e_1_3_2_17_2","first-page":"1528","article-title":"Filling delivery time automatically based on couriers\u2019 trajectories","volume":"35","author":"Ruan Sijie","year":"2021","unstructured":"Sijie Ruan, Xi Fu, Cheng Long, Jie Bao, Ruiyuan Li, Yiheng Chen, Shengnan Wu, and Yu Zheng. 2021. Filling delivery time automatically based on couriers\u2019 trajectories. IEEE Transactions on Knowledge and Data Engineering 35, 2 (2021), 1528\u20131540.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539027"},{"key":"e_1_3_2_19_2","first-page":"3241","volume-title":"Proceedings of the IEEE 38th International Conference on Data Engineering (ICDE\u201922)","author":"Ruan Sijie","year":"2022","unstructured":"Sijie Ruan, Cheng Long, Xiaodu Yang, Tianfu He, Ruiyuan Li, Jie Bao, Yiheng Chen, Shengnan Wu, Jiangtao Cui, and Yu Zheng. 2022b. Discovering actual delivery locations from mis-annotated couriers\u2019 trajectories. In Proceedings of the IEEE 38th International Conference on Data Engineering (ICDE\u201922). 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