Jove
Visualize
Contact Us

Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Prediction Intervals01:03

Prediction Intervals

3.0K
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. 
3.0K

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Jan 2, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.1K

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data.

Byoungsuk Ji1, Ellen J Hong2

  • 1Convergence Laboratory, KT R&D Center, Seoul 06763, Korea.

Sensors (Basel, Switzerland)
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method for real-time road traffic speed prediction using Long-Term Evolution (LTE) data. The model accurately forecasts traffic speeds, even in areas with poor data collection.

Keywords:
LTE access datacellular phonesdeep learninglong short-term memory (LSTM)road traffic prediction

Related Experiment Videos

Last Updated: Jan 2, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.1K

Area of Science:

  • Computer Science
  • Transportation Engineering
  • Data Science

Background:

  • Accurate real-time road traffic prediction is crucial for efficient transportation management.
  • Traditional traffic monitoring methods face limitations in coverage and accuracy, especially in challenging environments.

Purpose of the Study:

  • To develop a novel deep-learning-based system for real-time road traffic speed prediction.
  • To leverage Long-Term Evolution (LTE) access data for enhanced traffic prediction accuracy.

Main Methods:

  • A road traffic speed learning model was generated using historical road speed and LTE data from base stations.
  • Real-time LTE data served as input to the trained model for predicting current traffic speeds.
  • A time-series-based approach was employed to capture temporal traffic patterns.

Main Results:

  • The proposed system demonstrated effective real-time road traffic speed prediction.
  • The model's performance remained robust even with environmental changes or data collection issues.
  • Accuracy of real-time traffic predictions was improved, particularly in radio shadow areas.

Conclusions:

  • Deep learning utilizing LTE data offers a promising solution for accurate real-time traffic prediction.
  • The method enhances traffic monitoring capabilities, overcoming limitations of conventional approaches.
  • This approach facilitates reliable traffic speed estimation in diverse and challenging road conditions.