{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T07:08:00Z","timestamp":1765004880339,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T00:00:00Z","timestamp":1479772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Found for Distinguished Young Scholars of China","award":["41525004"],"award-info":[{"award-number":["41525004"]}]},{"DOI":"10.13039\/501100003999","name":"Science Fund for Creative Research Groups","doi-asserted-by":"publisher","award":["41421001"],"award-info":[{"award-number":["41421001"]}],"id":[{"id":"10.13039\/501100003999","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["41231171"],"award-info":[{"award-number":["41231171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals\u2019 average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day\u2019s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.<\/jats:p>","DOI":"10.3390\/s16111958","type":"journal-article","created":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T11:13:07Z","timestamp":1479813187000},"page":"1958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7694-564X","authenticated-orcid":false,"given":"Hua","family":"Shu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ci","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Pei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianming","family":"Xu","sequence":"additional","affiliation":[{"name":"RTmap Science and Technology Ltd., Beijing 100093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Ou","sequence":"additional","affiliation":[{"name":"RTmap Science and Technology Ltd., Beijing 100093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Libin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Technology Department, Beijing Capital International Airport Co., Ltd., Beijing 100621, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Information Technology Department, Beijing Capital International Airport Co., Ltd., Beijing 100621, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TMC.2015.2407400","article-title":"Queuesense: Collaborative recognition of queuing on mobile phones","volume":"15","author":"Li","year":"2016","journal-title":"IEEE. 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