{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:59:02Z","timestamp":1754161142859,"version":"3.41.2"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform","award":["No. 3502ZCQXT2021003"],"award-info":[{"award-number":["No. 3502ZCQXT2021003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2023,9,27]]},"abstract":"<jats:p>Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environment data, CrowdQ can accurately estimate emergency visits from noisy traffic flows. Furthermore, it employs queueing theory to model the complex emergency service process with medical service data, effectively considering spatiotemporal dependencies and event context impact on ED queue states. Experiments conducted on large-scale crowdsensing urban traffic datasets and hospital information system datasets from Xiamen City demonstrate the framework's effectiveness. It achieves an F1 score of 0.93 in ED demand identification, effectively models the ED queue state of key hospitals, and reduces the error in queue state prediction by 18.5%-71.3% compared to baseline methods. CrowdQ, therefore, offers valuable alternatives for public emergency treatment information disclosure and maximized medical resource allocation.<\/jats:p>","DOI":"10.1145\/3610875","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T15:45:03Z","timestamp":1695829503000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["CrowdQ"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3291-3739","authenticated-orcid":false,"given":"Tieqi","family":"Shou","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3724-9972","authenticated-orcid":false,"given":"Zhuohan","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8049-716X","authenticated-orcid":false,"given":"Yayao","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1611-2053","authenticated-orcid":false,"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1420-5966","authenticated-orcid":false,"given":"Hang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4857-7143","authenticated-orcid":false,"given":"Zhihan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-0422","authenticated-orcid":false,"given":"Dingqi","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Macau, Macau, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9141-8474","authenticated-orcid":false,"given":"Binbin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University City College, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-796X","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4554-6782","authenticated-orcid":false,"given":"Longbiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Why machine learning integrated patient flow simulation? arXiv preprint arXiv:2104.08203","author":"Abuhay Tesfamariam M","year":"2021","unstructured":"Tesfamariam M Abuhay, Adane Mamuye, Stewart Robinson, and Sergey V Kovalchuk. 2021. Why machine learning integrated patient flow simulation? arXiv preprint arXiv:2104.08203 (2021)."},{"volume-title":"Probability, statistics, and queueing theory","author":"Allen Arnold O","key":"e_1_2_1_2_1","unstructured":"Arnold O Allen. 2014. Probability, statistics, and queueing theory. Academic press."},{"key":"e_1_2_1_3_1","unstructured":"AMANDA MACMILLAN. 2018. Hospitals Overwhelmed by Flu Patients Are Treating Them in Tents. https:\/\/time.com\/5107984\/hospitals-handling-burden-flu-patients\/ Last accessed on 2023-03-10."},{"key":"e_1_2_1_4_1","volume-title":"Hasan Abolghasem Gorji, and Negar Feazbakhsh","author":"Amina Saeed","year":"2016","unstructured":"Saeed Amina, Ahmad Barrati, Jamil Sadeghifar, Marzyeh Sharifi, Zahra Toulideh, Hasan Abolghasem Gorji, and Negar Feazbakhsh. 2016. Measuring and analyzing waiting time indicators of patients' admitted in emergency department: a case study. Global journal of health science 8, 1 (2016), 143."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICITISEE.2018.8720979"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-012-9852-0"},{"volume-title":"An introduction to queueing theory: and matrix-analytic methods","author":"Breuer Lothar","key":"e_1_2_1_7_1","unstructured":"Lothar Breuer and Dieter Baum. 2005. An introduction to queueing theory: and matrix-analytic methods. Springer Science & Business Media."},{"key":"e_1_2_1_8_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)."},{"key":"e_1_2_1_9_1","volume-title":"Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe. Spatial statistics 49","author":"Bucci A","year":"2022","unstructured":"A Bucci, L Ippoliti, P Valentini, and S Fontanella. 2022. Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe. Spatial statistics 49 (2022), 100543."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.orhc.2017.05.001"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0183574"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_13_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-24221-6"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3565973"},{"key":"e_1_2_1_16_1","volume-title":"Queueing Models for Patient-Flow Dynamics in Inpatient Wardsa. Operations research 68, 1","author":"Dong Jing","year":"2019","unstructured":"Jing Dong and Ohad Perry. 2019. Queueing Models for Patient-Flow Dynamics in Inpatient Wardsa. Operations research 68, 1 (2019)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_1_19_1","first-page":"489","article-title":"M\/M\/c Queue with Two Priority Classes","volume":"63","author":"Scheller-Wolf Jianfu Alan","year":"2015","unstructured":"Alan Scheller-Wolf Jianfu Wang, Opher Baron. 2015. M\/M\/c Queue with Two Priority Classes. Operations Research 63, 3 (2015), 489--749.","journal-title":"Operations Research"},{"key":"e_1_2_1_20_1","volume-title":"A universal deep learning approach for modeling the flow of patients under different severities. Computer methods and programs in biomedicine 154","author":"Jiang Shancheng","year":"2018","unstructured":"Shancheng Jiang, Kwai-Sang Chin, and Kwok L Tsui. 2018. A universal deep learning approach for modeling the flow of patients under different severities. Computer methods and programs in biomedicine 154 (2018), 191--203."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-019-9034-z"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0107-0"},{"key":"e_1_2_1_23_1","volume-title":"International Conference on Web and Social Media. 445--453","author":"Carmen Karina Vaca Ruiz","year":"2015","unstructured":"Vaca Ruiz Carmen Karina, Daniele Quercia, Bonchi Francesco, Piero Fraternali, et al. 2015. Taxonomy-based discovery and annotation of functional areas in the city. In International Conference on Web and Social Media. 445--453."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161193"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.2219\/rtriqr.55.86"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvp.2020.101458"},{"key":"e_1_2_1_27_1","volume-title":"Reasons Healthcare Data Is Unique and Difficult to Measure. Health Catalyst","author":"LeSueur Dan","year":"2016","unstructured":"Dan LeSueur. 2016. Reasons Healthcare Data Is Unique and Difficult to Measure. Health Catalyst (2016)."},{"key":"e_1_2_1_28_1","volume-title":"United States","author":"Level Triage","year":"2010","unstructured":"Triage Level. [n. d.]. Median Emergency Department (ED) Wait and Treatment Times, by Triage Level---National Hospital Ambulatory Medical Care Survey, United States, 2010--2011. ([n. d.])."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2125163.2125192"},{"key":"e_1_2_1_30_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2012.2231973"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIMP.2001.925356"},{"key":"e_1_2_1_33_1","unstructured":"Dong LUO Tao CHENG Mingming DONG Fang SHEN Guihua YANG Yu CAO and Rong YAO. 2019. Characteristics of patients visiting emergency department by different ways of transportation. Chinese Journal of General Practitioners (2019) 847--850."},{"key":"e_1_2_1_34_1","volume-title":"Traffic flow prediction during the holidays based on DFT and SVR. Journal of Sensors 2019","author":"Luo Xianglong","year":"2019","unstructured":"Xianglong Luo, Danyang Li, and Shengrui Zhang. 2019. Traffic flow prediction during the holidays based on DFT and SVR. Journal of Sensors 2019 (2019)."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp.2018.00021"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019145927891"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.2174\/1874444301608010021"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2209201"},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 144--147","author":"Ruan Hailin","year":"2006","unstructured":"Hailin Ruan, Xiaomei Feng, Chunxu Yang, Aiguo Ding, Zhiqiang He, and Liuping Tang. 2006. Prospective Study on Epidemiology among the Emergency Patients in a Comprehensive Hospital. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 144--147."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3066164"},{"key":"e_1_2_1_41_1","volume-title":"Application of the five-level pediatric emergency triage system: a single center study. Zhonghua er ke za zhi= Chinese Journal of Pediatrics 56, 12","author":"Shi HX","year":"2018","unstructured":"HX Shi, JZ Wu, GB Chen, BZ Zhu, WY Yan, Ling Chen, YJ Xiao, and LY Zhang. 2018. Application of the five-level pediatric emergency triage system: a single center study. Zhonghua er ke za zhi= Chinese Journal of Pediatrics 56, 12 (2018), 933--938."},{"key":"e_1_2_1_42_1","unstructured":"Tieqi Shou Zhiyuan Wang Shang Shi Dingqi Yang Binbin Zhou Cheng Wang and Longbiao Chen. 2022. Modeling Crowdedness of Hospital Emergency Departments Leveraging Crowdsensing Mobility Data. In 2022 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI). IEEE."},{"key":"e_1_2_1_43_1","volume-title":"The emergency department as a complex system","author":"Smith Mark","year":"1999","unstructured":"Mark Smith and Craig Feied. 1999. The emergency department as a complex system. Washington: The George Washington University (1999)."},{"key":"e_1_2_1_44_1","first-page":"28","article-title":"ARIMA: The models of Box and Jenkins. Foresight","volume":"30","author":"Stellwagen Eric","year":"2013","unstructured":"Eric Stellwagen, Len Tashman, et al. 2013. ARIMA: The models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting 30 (2013), 28--33.","journal-title":"The International Journal of Applied Forecasting"},{"key":"e_1_2_1_45_1","volume-title":"Approximation of the non-stationary M (t)\/M (t)\/c (t)-queue using stationary queueing models: The stationary backlog-carryover approach. European Journal of operational research 190, 2","author":"Stolletz Raik","year":"2008","unstructured":"Raik Stolletz. 2008. Approximation of the non-stationary M (t)\/M (t)\/c (t)-queue using stationary queueing models: The stationary backlog-carryover approach. European Journal of operational research 190, 2 (2008), 478--493."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104541"},{"key":"e_1_2_1_47_1","volume-title":"A computer movie simulating urban growth in the Detroit region. Economic geography 46, sup1","author":"Tobler Waldo R","year":"1970","unstructured":"Waldo R Tobler. 1970. A computer movie simulating urban growth in the Detroit region. Economic geography 46, sup1 (1970), 234--240."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19095153"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2891258"},{"key":"e_1_2_1_50_1","volume-title":"Demand-Responsive Windows Scheduling in Tertiary Hospital Leveraging Spatiotemporal Neural Networks. In International Conference on Green, Pervasive, and Cloud Computing. Springer, 231--243","author":"Wang Zhiyuan","year":"2020","unstructured":"Zhiyuan Wang, Ruiying Guo, Linghong Hong, Cheng Wang, and Longbiao Chen. 2020. Demand-Responsive Windows Scheduling in Tertiary Hospital Leveraging Spatiotemporal Neural Networks. In International Conference on Green, Pervasive, and Cloud Computing. Springer, 231--243."},{"key":"e_1_2_1_51_1","volume-title":"Mobile sensing in the COVID-19 era: A review. Health Data Science","author":"Wang Zhiyuan","year":"2022","unstructured":"Zhiyuan Wang, Haoyi Xiong, Mingyue Tang, Mehdi Boukhechba, Tabor E Flickinger, and Laura E Barnes. 2022. Mobile sensing in the COVID-19 era: A review. Health Data Science (2022)."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161046"},{"key":"e_1_2_1_53_1","volume-title":"disinfection, and cross-colonization: are hospital surfaces reservoirs for nosocomial infection? Clinical infectious diseases 39, 8","author":"Weinstein Robert A","year":"2004","unstructured":"Robert A Weinstein and Bala Hota. 2004. Contamination, disinfection, and cross-colonization: are hospital surfaces reservoirs for nosocomial infection? Clinical infectious diseases 39, 8 (2004), 1182--1189."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11134-012-9291-0"},{"key":"e_1_2_1_55_1","volume-title":"Nasib Singh Gill, and Jyotir Moy Chatterjee","author":"Yadav Sangeeta","year":"2022","unstructured":"Sangeeta Yadav, Preeti Gulia, Nasib Singh Gill, and Jyotir Moy Chatterjee. 2022. A real-time crowd monitoring and management system for social distance classification and healthcare using deep learning. Journal of Healthcare Engineering 2022 (2022)."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18041261"},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Mao Ye Peifeng Yin Wang-Chien Lee and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Chinese Journal of Emergency Medicine. 325--334.","DOI":"10.1145\/2009916.2009962"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339561"},{"key":"e_1_2_1_60_1","volume-title":"AMIA Annual Symposium Proceedings","volume":"2003","author":"Zakaria Nasriah","year":"2003","unstructured":"Nasriah Zakaria, Jeffrey Stanton, and Kathryn Stam. 2003. Exploring security and privacy issues in hospital information system: An information boundary theory perspective. In AMIA Annual Symposium Proceedings, Vol. 2003. American Medical Informatics Association, 1059."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2015.05.002"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.23"},{"key":"e_1_2_1_63_1","volume-title":"IEEE International Conference on Computer Communications (INFOCOM).","author":"Zhang Desheng","year":"2011","unstructured":"Desheng Zhang, Tian He, FY Raghu, GD Zhang, THR Ganti, and H Lei. 2011. Where is the crowd?: crowdedness detection scheme for mobile crowdsensing applications. In IEEE International Conference on Computer Communications (INFOCOM)."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2891537"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1059-x"},{"key":"e_1_2_1_66_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629592","article-title":"Urban computing: concepts, methodologies, and applications","volume":"5","author":"Zheng Yu","year":"2014","unstructured":"Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 3 (2014), 1--55.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3610875","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3610875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T16:27:33Z","timestamp":1753720053000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3610875"}},"subtitle":["Predicting the Queue State of Hospital Emergency Department Using Crowdsensing Mobility Data-Driven Models"],"short-title":[],"issued":{"date-parts":[[2023,9,27]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9,27]]}},"alternative-id":["10.1145\/3610875"],"URL":"https:\/\/doi.org\/10.1145\/3610875","relation":{},"ISSN":["2474-9567"],"issn-type":[{"type":"electronic","value":"2474-9567"}],"subject":[],"published":{"date-parts":[[2023,9,27]]},"assertion":[{"value":"2023-09-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}