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

Modeling with Differential Equations01:25

Modeling with Differential Equations

137
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
137
Population Growth00:57

Population Growth

29.3K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
29.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

313
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...
313
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.2K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.1K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.1K

You might also read

Related Articles

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

Sort by
Same author

[Fluid management in orthotopic liver transplantation].

Zhongguo wei zhong bing ji jiu yi xue = Chinese critical care medicine = Zhongguo weizhongbing jijiuyixue·2006
Same author

Electromagnetic modelling of Raman enhancement from nanoscale substrates: a route to estimation of the magnitude of the chemical enhancement mechanism in SERS.

Faraday discussions·2006
Same author

[Experimental study on protective effects of HupA in the treatment of isocarbophos poisoning].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2006
Same author

[Complete sequence and gene organization of the Tibetan chicken mitochondrial genome].

Yi chuan = Hereditas·2006
Same author

Liver microcirculation after hepatic artery embolization with degradable starch microspheres in vivo.

World journal of gastroenterology·2006
Same author

A recyclable fluorous (S)-pyrrolidine sulfonamide promoted direct, highly enantioselective Michael addition of ketones and aldehydes to nitroolefins in water.

Organic letters·2006

Related Experiment Video

Updated: Mar 7, 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

Network capacity with probit-based stochastic user equilibrium problem.

Lili Lu1,2,3, Jian Wang4, Pengjun Zheng1,2,3

  • 1Faculty of Maritime and Transportation, Ningbo University, Ningbo, China.

Plos One
|February 9, 2017
PubMed
Summary
This summary is machine-generated.

The Probit-based stochastic user equilibrium (SUE) model offers a more realistic traffic assignment than the Logit-based SUE model. Both models show similar network capacity patterns with improved traveler information, reaching maximum capacity at higher information levels.

Related Experiment Videos

Last Updated: Mar 7, 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

Area of Science:

  • Transportation Engineering
  • Traffic Flow Theory
  • Operations Research

Background:

  • Stochastic user equilibrium (SUE) models are crucial for traffic assignment, with Logit-based models widely used despite limitations.
  • Logit-based SUE's assumption of identical and irrelevant alternatives (IIA) restricts its ability to handle route overlaps and perception variance.
  • Probit-based SUE models offer greater consistency with driver behavior but are less explored.

Purpose of the Study:

  • To explore network capacity using the Probit-based SUE traffic assignment model.
  • To investigate and compare network capacity differences between Probit-based and Logit-based SUE models.
  • To analyze the impact of traveler information on network capacity under both SUE models.

Main Methods:

  • Formulated network capacity as a bi-level programming problem.
  • Optimized input parameters (O-D multiplies, signal splits) in the upper-level program to maximize network capacity.
  • Employed Logit-based or Probit-based SUE as the lower-level problem to model driver route choice.
  • Developed a heuristic algorithm based on SUE sensitivity analysis to solve the bi-level program.

Main Results:

  • Network capacity values differ between Probit-based and Logit-based SUE constraints.
  • The variation pattern of network capacity with increasing traveler information is consistent across both SUE models.
  • Both Probit-based and Logit-based SUE models can achieve the same maximum network capacity at sufficient traveler information levels.

Conclusions:

  • The Probit-based SUE model provides a more nuanced representation of driver behavior in traffic assignment.
  • Network capacity analysis reveals distinct outcomes between Logit and Probit SUE models.
  • Traveler information significantly influences network capacity, with similar trends observed for both Logit and Probit SUE.