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

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time

250
When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
One of the key parameters is the mean transit time (MTT), which refers to the total duration required for drug molecules to transit through the body. MTT is determined by calculating the ratio of the area under the moment curve to the area...
250
Evaluating Limits by Direct Substitution01:29

Evaluating Limits by Direct Substitution

54
In the analysis of functions that represent continuous physical phenomena, it is often necessary to determine the output value as the input approaches a specific point. When a combination of algebraic terms defines the function and exhibits no discontinuities or abrupt changes near the point of interest, the limit of the function can be evaluated directly. This process, known as direct substitution, involves replacing the variable in the expression with the value it approaches.Direct...
54
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

203
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
203
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

425
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
425
Social Traps01:41

Social Traps

25.7K
Social traps are negative situations where people get caught in a direction or relationship that later proves to be unpleasant, with no easy way to back out of or avoid. The concept was orignally introduced by John Platt who applied psychology to Garrett Hardin's "Tragedy of the Commons", where in New England herd owners could let their cattle graze in the common ground. This situation seems like a good idea, but an individual could have an advantage. If they owned...
25.7K
Introduction to Functions01:29

Introduction to Functions

85
Functions are essential mathematical tools used to describe consistent relationships between varying quantities. A function connects each input to a single, corresponding output based on a defined rule. These relationships appear in both everyday contexts and natural phenomena, providing a framework for understanding change and prediction.One common real-life example is a parking garage fee system, where the total cost depends on the amount of time a vehicle remains inside. In this case, the...
85

You might also read

Related Articles

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

Sort by
Same author

Noise Levels Due to Commercial and Leisure Activities in Urban Areas: Experimental Validation of a Numerical Model Fed with Crowd Density Estimation Using Computer Vision.

Sensors (Basel, Switzerland)·2025
Same author

Data mining methodology for obtaining epidemiological data in the context of road transport systems.

Journal of ambient intelligence and humanized computing·2022
Same author

Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs.

Animals : an open access journal from MDPI·2022
Same author

Recent advances and current trends in brain-computer interface research and their applications.

International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience·2021
Same author

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments.

Sensors (Basel, Switzerland)·2020
Same author

Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Nov 27, 2025

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

3.9K

Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems.

Teresa Cristóbal1, Gabino Padrón1, Javier Lorenzo-Navarro2

  • 1Institute for Cybernetics, Campus de Tafira, Las Palmas de Gran Canaria, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Understanding public transit demand is key for efficient systems. This study introduces new time-dependent attributes, derived from data mining and clustering, to better predict user needs and improve transit planning.

Keywords:
attribute creationclusteringdata miningdemandentropyintelligent transport systemsmass transit systems

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.7K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.9K

Related Experiment Videos

Last Updated: Nov 27, 2025

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

3.9K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.7K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.9K

Area of Science:

  • Transportation Science
  • Data Mining
  • Operations Research

Background:

  • Efficient mass transit systems are crucial for modern societies.
  • Understanding user demand is essential for designing and planning effective transit systems.
  • Traditional demand attributes may not fully capture user behavior.

Purpose of the Study:

  • To propose novel time-dependent attributes for representing user demand in mass transit.
  • To demonstrate the effectiveness of these new attributes in improving demand understanding and prediction.
  • To provide a more precise methodology for transit system design and planning.

Main Methods:

  • Utilized data mining techniques, specifically clustering, to derive new time-dependent demand attributes.
  • Employed Shannon entropy and neural networks to evaluate the quality and predictive power of the new attributes.
  • Implemented and validated the methodology on an intercity public transport company.

Main Results:

  • The newly developed time-dependent attributes provide a more accurate representation of user demand.
  • The proposed methodology enables demand prediction with acceptable precision.
  • The findings offer significant improvements over traditional methods for transit demand analysis.

Conclusions:

  • The novel time-dependent attributes enhance the understanding of user demand in mass transit systems.
  • This data-driven approach improves the precision of demand prediction for better transit planning.
  • The methodology is effective for real-world applications in public transportation companies.