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Sensors 2016, 16(11), 1958; doi:10.3390/s16111958

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
RTmap Science and Technology Ltd., Beijing 100093, China
4
Information Technology Department, Beijing Capital International Airport Co., Ltd., Beijing 100621, China
*
Author to whom correspondence should be addressed.
Academic Editor: Fan Ye
Received: 19 July 2016 / Revised: 27 October 2016 / Accepted: 16 November 2016 / Published: 22 November 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3562 KB, uploaded 22 November 2016]   |  

Abstract

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. View Full-Text
Keywords: indoor queuing time; WiFi positioning; trajectory; mobile; time series analysis indoor queuing time; WiFi positioning; trajectory; mobile; time series analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shu, H.; Song, C.; Pei, T.; Xu, L.; Ou, Y.; Zhang, L.; Li, T. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario. Sensors 2016, 16, 1958.

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