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

11.0K
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.
11.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.1K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.1K
Construction of Frequency Distribution01:15

Construction of Frequency Distribution

7.5K
A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
7.5K
What is a Frequency Distribution00:51

What is a Frequency Distribution

19.7K
A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
19.7K
The Availability Heuristic01:08

The Availability Heuristic

5.9K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
5.9K

You might also read

Related Articles

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

Sort by
Same author

The Intersection of Human Rights, HIV and Sex Work: Implications for Ending AIDS in Thailand by 2030.

Journal of the International AIDS Society·2026
Same author

An antibiotic chatbot: Evaluation of a retrieval-augmented generation approach for providing guideline-based antimicrobial advice.

The Journal of infection·2026
Same author

Early stroke specialist vocational rehabilitation for REturn To work After stroKE: a synopsis from the RETAKE RCT.

Health technology assessment (Winchester, England)·2026
Same author

Reflective interventions for cybersecurity: insights from a sociotechnical framework application and assessment.

Cognition, technology & work (Online)·2026
Same author

Development and description of Early Stroke Specialist Vocational Rehabilitation delivered in the RETAKE trial.

Health technology assessment (Winchester, England)·2026
Same author

Home-based extended rehabilitation for older people with frailty (HERO): a randomised controlled trial.

Age and ageing·2026

Related Experiment Video

Updated: May 15, 2025

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

821

Passenger information function preferences based on travel frequency and expertise.

Shalaka Kurup1, David Golightly2, Sarah Sharples1

  • 1Human Factors Research Group, University of Nottingham, Nottingham, UK.

Ergonomics
|April 8, 2025
PubMed
Summary

Rail passenger experience is enhanced by personalized information. Passenger travel frequency and expertise influence information needs, particularly for disruption alerts and basic trip support.

Keywords:
Passenger informationexpertiseinformation functionsrailuser-centred personalisation

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
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

3.2K

Related Experiment Videos

Last Updated: May 15, 2025

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

821
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
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

3.2K

Area of Science:

  • Human-Computer Interaction
  • Transportation Science
  • Cognitive Psychology

Background:

  • User-centered design is crucial for optimizing the rail passenger experience.
  • Passenger information systems significantly impact travel satisfaction and usability.
  • Understanding user expertise and travel frequency is key to effective information delivery.

Purpose of the Study:

  • To investigate how passenger travel frequency and self-reported expertise influence the perceived usefulness of various rail information functions.
  • To explore the potential for personalizing passenger information based on user expertise and travel habits.
  • To identify specific information needs that vary with passenger experience levels.

Main Methods:

  • A survey of 293 rail passengers assessed travel frequency, self-reported travel knowledge, and the usefulness of 36 distinct rail information functions.
  • Factor analysis was employed to group information functions and examine their relationships with user characteristics.
  • Statistical analysis was used to determine the differential effects of travel frequency and expertise on information preferences.

Main Results:

  • A strong correlation was confirmed between passenger trip frequency and self-reported travel expertise.
  • Factor analysis revealed six distinct groups of information functions, with varying preferences based on travel frequency and expertise, although these factors explained limited variance.
  • Significant differential effects were observed for critical, unfactored information functions, highlighting nuanced user needs.

Conclusions:

  • Personalization of rail passenger information based on travel frequency and expertise shows partial support.
  • Disruption information and support for basic trip activities are key areas where personalization can be most beneficial.
  • Tailoring information delivery can improve the experience for both novice and frequent rail travelers.