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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

96
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
96
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

137
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
137
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

244
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
244
Probability Histograms01:17

Probability Histograms

12.3K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

112
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
112

You might also read

Related Articles

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

Sort by
Same author

Cecal bascule in a patient with complete situs inversus-a rare cause of left upper quadrant abdominal pain.

Journal of surgical case reports·2026
Same author

Energy Distribution of the Galactic Center Excess's Sources.

Physical review letters·2026
Same author

Childhood exposure to traffic-related air pollution and lung function at age 10 years: evidence of nutritional modification in a birth cohort.

International journal of hygiene and environmental health·2026
Same author

Factors associated with glycemic control in Korean older adults with diabetes living alone: A secondary analysis.

Journal of Korean gerontological nursing·2026
Same author

300-unit-per-second roll-to-roll manufacturing of visible metalenses.

Nature·2026
Same author

Active aeration enhances tissue hydration and fresh mass in hydroponic lettuce and modulates calcium-mobilizing biostimulant efficacy.

Scientific reports·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Oct 3, 2025

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

13.7K

Machine learning approach for study on subway passenger flow.

Yujin Park1, Yoonhee Choi1, Kyongwon Kim2

  • 1Department of Statistics, Ewha Womans University, Seoul, 03760, South Korea.

Scientific Reports
|February 18, 2022
PubMed
Summary
This summary is machine-generated.

This study predicts subway passenger flow using clustering and time series analysis. Incorporating regional data improves prediction accuracy, aiding transit planning and smart city development.

More Related Videos

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

3.5K

Related Experiment Videos

Last Updated: Oct 3, 2025

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

13.7K
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

3.5K

Area of Science:

  • Urban Planning
  • Transportation Science
  • Data Science

Background:

  • Accurate prediction of subway passenger flow is crucial for efficient urban transit systems.
  • Understanding regional variations in passenger transport patterns is key to improving service.
  • Existing methods may not fully leverage geographical information for flow prediction.

Purpose of the Study:

  • To develop a two-step procedure for predicting subway passenger transport flow.
  • To incorporate regional features and geographical information into time series prediction models.
  • To enhance the accuracy of passenger flow predictions by considering station-specific patterns.

Main Methods:

  • Utilized a massive smart card transaction dataset from Seoul Metro.
  • Applied the funFEM clustering method to categorize subway stations based on transport patterns.
  • Implemented a two-step prediction process combining cluster analysis with functional time series prediction.

Main Results:

  • Subway stations were successfully clustered into six distinct categories.
  • The proposed two-step prediction method demonstrated higher accuracy compared to models ignoring regional properties.
  • The data-driven approach effectively captured daily passenger numbers for each station and cluster.

Conclusions:

  • Clustering stations by regional transport patterns significantly improves passenger flow prediction accuracy.
  • The findings support enhanced subway service planning, including congestion reduction and infectious disease mitigation.
  • The prediction model offers valuable insights for smart city initiatives focused on sustainable urban mobility.