Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Errors in Global Positioning System01:26

Errors in Global Positioning System

384
Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
384
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

349
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
349
The Midpoint Formula01:24

The Midpoint Formula

6.9K
In coordinate geometry, determining the central point between two locations is common. This central point, or midpoint, lies exactly halfway along the line segment connecting two points in a two-dimensional space. It has applications in mathematics, physics, engineering, and various planning disciplines.Given two points labeled as A (x1, y1) and B (x2, y2) on a coordinate plane, a straight line segment can be plotted between them. The midpoint, labeled point M, divides this segment into two...
6.9K
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

407
GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
407
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

438
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
438
Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cohort Profile: The Nanjing Chronic Disease Cohort.

International journal of epidemiology·2026
Same author

Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.

Digital health·2026
Same author

Segmental Glomerulosclerosis Subclassification in the Oxford Classification System (MEST-C) Improves the International IgA Nephropathy Prediction Tool.

Journal of clinical medicine·2026
Same author

Association between preoperative metformin exposure and postoperative nausea and vomiting in patients undergoing general anaesthesia: a protocol for a prospective observational cohort study in a Chinese tertiary hospital.

BMJ open·2026
Same author

Stage-Specific Lifestyle Effects on the Dynamic Transitions of Metabolic Multimorbidity - Jiangsu Province, China, 2019-2024.

China CDC weekly·2026
Same author

Alpinetin alleviates sepsis-induced cardiomyopathy by suppressing serine/threonine protein phosphatase 1γ-mediated ROS accumulation.

European journal of pharmacology·2026

Related Experiment Video

Updated: Mar 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.

Hua Shu1,2, Ci Song3, Tao Pei4

  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China. shuh@lreis.ac.cn.

Sensors (Basel, Switzerland)
|November 24, 2016
PubMed
Summary

This study introduces a new method using WiFi data to estimate and predict indoor queuing times. The nonstandard autoregressive (NAR) model offers improved accuracy for managing wait times in public spaces.

Keywords:
WiFi positioningindoor queuing timemobiletime series analysistrajectory

More Related Videos

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.3K

Related Experiment Videos

Last Updated: Mar 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.3K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Queuing is prevalent in urban public spaces, impacting individual experience and resource management.
  • Accurate monitoring and prediction of queuing times are essential for efficiency and emergency response.

Purpose of the Study:

  • To propose a novel method for estimating and predicting indoor queuing times using WiFi positioning data.
  • To enhance resource allocation and emergency preparedness in public venues.

Main Methods:

  • Utilizing WiFi positioning data to identify representative queuing trajectories.
  • Estimating average queuing times within defined daily time slices.
  • Developing a nonstandard autoregressive (NAR) model trained on historical data for prediction.
  • Optimizing positioning system deployment and introducing a drift ratio parameter to mitigate topological errors.

Main Results:

  • The NAR model demonstrated superior precision compared to other time series models.
  • A case study at Beijing Capital International Airport showed a mean absolute estimation error of 147s (26.92%) and a prediction error of 27.49%.
  • The method effectively alleviates the impact of WiFi positioning errors on estimation accuracy.

Conclusions:

  • The proposed NAR model is an effective tool for estimating and predicting queuing times in indoor public areas.
  • The approach offers practical benefits for both individuals managing wait times and managers optimizing operations.