{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T18:57:36Z","timestamp":1774983456672,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFB2100300"],"award-info":[{"award-number":["2018YFB2100300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61925202"],"award-info":[{"award-number":["61925202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens\u2019 daily lives significantly. In the fulfillment cycle, the order preparation time estimation is extremely important and can be used for many applications, such as improving order dispatching and fulfillment time estimation. Existing work is generally based on high-cost physical devices or large-scale labeled training data, which are not feasible in on-demand delivery services. We solve this problem based on already collected different kinds of data from the on-demand delivery platform, e.g., the courier\u2019s reported arrival time to the merchant. Our intuition is that the couriers\u2019 reported time implicitly reflects the order preparation time, which leads to a challenge: complicated correlations between the couriers\u2019 reported arrival time and the order preparation time. To solve this challenge, we design an order preparation time inference framework OPTI, which first constructs a self-supervised classification task based on the couriers\u2019 reported arrival time to infer the coarse-grained order preparation time and then exploits semi-supervised learning to transfer the coarse-grained time to fine-grained time inference. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.<\/jats:p>","DOI":"10.1145\/3592610","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T12:39:10Z","timestamp":1681389550000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["OPTI: Order Preparation Time Inference for On-demand Delivery"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8616-0202","authenticated-orcid":false,"given":"Zhigang","family":"Dai","sequence":"first","affiliation":[{"name":"Peking University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2954-3901","authenticated-orcid":false,"given":"Wenjun","family":"Lyu","sequence":"additional","affiliation":[{"name":"Rutgers University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1226-341X","authenticated-orcid":false,"given":"Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3730-4457","authenticated-orcid":false,"given":"Yiwei","family":"Song","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-8078","authenticated-orcid":false,"given":"Yunhuai","family":"Liu","sequence":"additional","affiliation":[{"name":"Peking University"}]}],"member":"320","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2018. The Loss Function of MultiClass in Catboost. [EB\/OL]. Retrieved from https:\/\/catboost.ai\/docs\/concepts\/loss-functions-multiclassification.html."},{"key":"e_1_3_1_3_2","unstructured":"2019. Eleme. 2019. Eleme. Webpage. (2019)."},{"key":"e_1_3_1_4_2","unstructured":"2020. Amazon. 2020. Amzon Prime Now. Webpage. (2020)."},{"key":"e_1_3_1_5_2","unstructured":"2020. Meituan. 2020. Meituan. Webpage. (2020)."},{"key":"e_1_3_1_6_2","unstructured":"2020. Postmates. 2020. Postmates. Webpage. (2020)."},{"key":"e_1_3_1_7_2","unstructured":"2020. Uber. 2020. Uber Eat. Webpage. (2020)."},{"key":"e_1_3_1_8_2","volume-title":"Proceedings of the ICML 2005 Workshop on Learning with Partially Classified Training Data","author":"Balcan Maria-Florina","year":"2005","unstructured":"Maria-Florina Balcan, Avrim Blum, Patrick Pakyan Choi, John Lafferty, Brian Pantano, Mugizi Robert Rwebangira, and Xiaojin Zhu. 2005. Person identification in webcam images: An application of semi-supervised learning. In Proceedings of the ICML 2005 Workshop on Learning with Partially Classified Training Data."},{"issue":"1","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1177\/001391657500700102","article-title":"Distance estimation in cities","volume":"7","author":"Canter David","year":"1975","unstructured":"David Canter and Stephen K. Tagg. 1975. Distance estimation in cities. Environment and Behavior 7, 1 (1975), 59\u201380.","journal-title":"Environment and Behavior"},{"issue":"6","key":"e_1_3_1_10_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. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2016), 1478\u20131496.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_11_2","first-page":"859","volume-title":"Proceedings of the 18th Symposium on Networked Systems Design and Implementation","author":"Ding Yi","year":"2021","unstructured":"Yi Ding, Ling Liu, Yu Yang, Yunhuai Liu, Desheng Zhang, and Tian He. 2021. From conception to retirement: A lifetime story of a 3-year-old wireless beacon system in the wild. In Proceedings of the 18th Symposium on Networked Systems Design and Implementation. 859\u2013872."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.167"},{"key":"e_1_3_1_13_2","unstructured":"Anna Veronika Dorogush Vasily Ershov and Andrey Gulin. 2018. CatBoost: Gradient boosting with categorical features support. arXiv:1810.11363. Retrieved from https:\/\/arxiv.org\/abs\/1810.11363."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2021068"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/1254882.1254934"},{"issue":"1","key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10696-011-9125-0","article-title":"Integrated production and distribution planning for perishable food products","volume":"24","author":"Farahani Poorya","year":"2012","unstructured":"Poorya Farahani, Martin Grunow, and H.-O. G\u00fcnther. 2012. Integrated production and distribution planning for perishable food products. Flexible Services and Manufacturing Journal 24, 1 (2012), 28\u201351.","journal-title":"Flexible Services and Manufacturing Journal"},{"key":"e_1_3_1_17_2","unstructured":"Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv:1803.07728. Retrieved from https:\/\/arxiv.org\/abs\/1803.07728."},{"key":"e_1_3_1_18_2","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. arXiv:1703.04247. Retrieved from https:\/\/arxiv.org\/abs\/1703.04247."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313464"},{"key":"e_1_3_1_20_2","first-page":"3146","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017), 3146\u20133154.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_21_2","unstructured":"Diederik P. Kingma Danilo J. Rezende Shakir Mohamed and Max Welling. 2014. Semi-supervised learning with deep generative models. arXiv:1406.5298. Retrieved from https:\/\/arxiv.org\/abs\/1406.5298."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169689"},{"key":"e_1_3_1_23_2","first-page":"279","volume-title":"Proceedings of the 20th International Conference on Advances in Geographic Information Systems","author":"Lee Wang-Chien","year":"2012","unstructured":"Wang-Chien Lee, Weiping Si, Ling-Jyh Chen, and Meng Chang Chen. 2012. HTTP: A new framework for bus travel time prediction based on historical trajectories. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems. 279\u2013288."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313418"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220033"},{"issue":"6","key":"e_1_3_1_26_2","first-page":"1288","article-title":"Foodnet: Toward an optimized food delivery network based on spatial crowdsourcing","volume":"18","author":"Liu Yan","year":"2018","unstructured":"Yan Liu, Bin Guo, Chao Chen, He Du, Zhiwen Yu, Daqing Zhang, and Huadong Ma. 2018. Foodnet: Toward an optimized food delivery network based on spatial crowdsourcing. IEEE Transactions on Mobile Computing 18, 6 (2018), 1288\u20131301.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_27_2","first-page":"1","volume-title":"Proceedings of the IEEE Southeastcon","author":"Salman Raied","year":"2012","unstructured":"Raied Salman and Vojislav Kecman. 2012. Regression as classification. In Proceedings of the IEEE Southeastcon. IEEE, 1\u20136."},{"key":"e_1_3_1_28_2","unstructured":"Yu Sun Eric Tzeng Trevor Darrell and Alexei A. Efros. 2019. Unsupervised domain adaptation through self-supervision. arXiv:1909.11825. Retrieved from https:\/\/arxiv.org\/abs\/1909.11825."},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1007\/11671299_52","volume-title":"Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics","author":"Suzuki Yasuhiro","year":"2006","unstructured":"Yasuhiro Suzuki, Hiroya Takamura, and Manabu Okumura. 2006. Application of semi-supervised learning to evaluative expression classification. In Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 502\u2013513."},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"issue":"2","key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3293317","article-title":"A simple baseline for travel time estimation using large-scale trip data","volume":"10","author":"Wang Hongjian","year":"2019","unstructured":"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\u201322.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_1_33_2","first-page":"1","volume-title":"Proceedings of the 26th Annual International Conference on Mobile Computing and Networking","author":"Yang Yu","year":"2020","unstructured":"Yu Yang, Yi Ding, Dengpan Yuan, Guang Wang, Xiaoyang Xie, Yunhuai Liu, Tian He, and Desheng Zhang. 2020. TransLoc: Transparent indoor localization with uncertain human participation for instant delivery. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1\u201314."},{"key":"e_1_3_1_34_2","first-page":"188","volume-title":"Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design","author":"Yuan Qiming","year":"2019","unstructured":"Qiming Yuan, Rong Zhao, and Jin Zhang. 2019. FoodCarpool: A negotiation-based carpooling system for take-out food delivery. In Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design. IEEE, 188\u2013193."},{"key":"e_1_3_1_35_2","first-page":"1476","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Zhai Xiaohua","year":"2019","unstructured":"Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer. 2019. S4l: Self-supervised semi-supervised learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 1476\u20131485."},{"issue":"3","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3351282","article-title":"Route prediction for instant delivery","volume":"3","author":"Zhang Yan","year":"2019","unstructured":"Yan Zhang, Yunhuai Liu, Genjian Li, Yi Ding, Ning Chen, Hao Zhang, Tian He, and Desheng Zhang. 2019. Route prediction for instant delivery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1\u201325.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_1_37_2","first-page":"1239","volume-title":"Proceedings of the Academic Press Library in Signal Processing","author":"Zhou Xueyuan","year":"2014","unstructured":"Xueyuan Zhou and Mikhail Belkin. 2014. Semi-supervised learning. In Proceedings of the Academic Press Library in Signal Processing. 1239\u20131269."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403307"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00196ED1V01Y200906AIM006"},{"key":"e_1_3_1_40_2","unstructured":"Xiaojin Jerry Zhu. 2005. Semi-supervised learning literature survey. (2005)."}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592610","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3592610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:56Z","timestamp":1750183736000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,11,30]]}},"alternative-id":["10.1145\/3592610"],"URL":"https:\/\/doi.org\/10.1145\/3592610","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]},"assertion":[{"value":"2022-06-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}