Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario
文献类型：期刊论文
| 作者 | Shu, Hua2,3; Song, Ci2; Pei, Tao2; Xu, Lianming1; Ou, Yang1; Zhang, Libin4; Li, Tao4 |
| 刊名 | SENSORS
 |
| 出版日期 | 2016-11-01 |
| 卷号 | 16期号:11页码:20 |
| 关键词 | indoor queuing time WiFi positioning trajectory mobile time series analysis |
| ISSN号 | 1424-8220 |
| DOI | 10.3390/s16111958 |
| 通讯作者 | Pei, Tao(peit@lreis.ac.cn) |
| 英文摘要 | 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' average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day's 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. |
| WOS关键词 | WEIGHTED MOVING AVERAGES ; FRAMEWORK ; SYSTEMS ; STATE |
| 资助项目 | National Science Found for Distinguished Young Scholars of China[41525004] ; Science Fund for Creative Research Groups[41421001] ; NSFC[41231171] |
| WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000389641700191 |
| 出版者 | MDPI AG |
| 资助机构 | National Science Found for Distinguished Young Scholars of China ; Science Fund for Creative Research Groups ; NSFC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/65480]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Pei, Tao |
| 作者单位 | 1.RTmap Sci & Technol Ltd, Beijing 100093, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Beijing Capital Int Airport Co Ltd, Dept Informat Technol, Beijing 100621, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shu, Hua,Song, Ci,Pei, Tao,et al. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario[J]. SENSORS,2016,16(11):20. |
| APA | Shu, Hua.,Song, Ci.,Pei, Tao.,Xu, Lianming.,Ou, Yang.,...&Li, Tao.(2016).Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.SENSORS,16(11),20. |
| MLA | Shu, Hua,et al."Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario".SENSORS 16.11(2016):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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