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

Probability Histograms01:17

Probability Histograms

12.8K
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.
12.8K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

408
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
408
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

617
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
617
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

151
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...
151
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

827
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
827
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

67
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
67

You might also read

Related Articles

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

Sort by
Same author

Investigating the spatial effects of zonal factors on road traffic speed variability during peak hour.

PloS one·2026
Same author

Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency.

International journal of environmental research and public health·2022
Same author

A Descriptive Analysis on the Impact of COVID-19 Lockdowns on Road Traffic Incidents in Sydney, Australia.

International journal of environmental research and public health·2021
Same author

A simple crowdsourced delay-based traffic signal control.

PloS one·2020

Related Experiment Video

Updated: Nov 18, 2025

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

Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent.

Sai Chand1

  • 1Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Entropy (Basel, Switzerland)
|February 6, 2021
PubMed
Summary

Traffic count predictability is crucial for transport planning. This study found traffic predictability exceeds 80%, influenced by factors like day of the week and weather, using fractal theory and regression models.

Keywords:
Hurst exponentintersectionspredictabilitytraffic count

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

3.9K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K

Related Experiment Videos

Last Updated: Nov 18, 2025

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
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
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K

Area of Science:

  • Transportation Science
  • Fractal Theory
  • Statistical Modeling

Background:

  • Predictability is vital for transport planning, but time-varying traffic phenomena present challenges.
  • Sources of unpredictability include data uncertainty, model limitations, and process complexity.
  • Understanding traffic count predictability is key for accurate predictions and method selection.

Purpose of the Study:

  • To quantify the predictability of traffic counts at signalized intersections using fractal theory.
  • To identify and quantify the impact of various factors on traffic count predictability.

Main Methods:

  • Utilized the Hurst exponent from fractal theory to measure traffic count fluctuations and predictability.
  • Collected data from 37 intersections in Sydney, Australia over one year.
  • Developed a random-effects linear regression model to analyze influencing factors.

Main Results:

  • Theoretical predictability of traffic counts at signalized intersections is generally above 80%.
  • Predictability is strongly associated with the day of the week, with public holidays, special events, and weekends being more predictable than weekdays.
  • Rainfall negatively impacts predictability, while more parking spaces at intersections correlate with higher predictability.

Conclusions:

  • Traffic counts at signalized intersections exhibit high inherent predictability.
  • Factors such as day type, weather, and roadside infrastructure significantly influence traffic predictability.
  • Findings provide essential insights for improving traffic prediction models and planning.