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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

656
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:
656
Survival Tree01:19

Survival Tree

453
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
453
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.6K
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...
2.6K

You might also read

Related Articles

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

Sort by
Same author

A multi-dimensional human factors evaluation of exoskeleton and adaptive cobot use in automotive assembly.

Ergonomics·2026
Same author

The Gender-Equality Paradox: A Longitudinal Test of the Free Expression or Resource Hypothesis Among Adolescents.

Psychological reports·2026
Same author

Satisfaction in human-DSS interaction is interactively modulated by broad DSS representations and single interactions.

Frontiers in artificial intelligence·2026
Same author

Dynamic velocity scaling for industrial collaborative robots: a gaze-driven approach.

Scientific reports·2026
Same author

Time Trends in Peer Violence and Bullying Across Countries and Regions of Europe, Central Asia, and Canada Among Students Aged 11, 13, and 15 from 2013 to 2022.

Behavioral sciences (Basel, Switzerland)·2026
Same author

COVID-19 stress, aggressiveness, and deviant behaviour: a mediation analysis of youth in the pandemic era.

Australian journal of psychology·2026

Related Experiment Video

Updated: Mar 7, 2026

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
07:30

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

9.4K

Using data mining techniques to predict the severity of bicycle crashes.

Gabriele Prati1, Luca Pietrantoni1, Federico Fraboni1

  • 1Dipartimento di Psicologia, Università di Bologna, Viale Europa 115, 47521 Cesena, FC, Italy.

Accident; Analysis and Prevention
|February 12, 2017
PubMed
Summary

This study identified key factors predicting bicycle crash severity in Italy. Road type, crash type, and cyclist age were the most significant predictors, informing safety strategies.

Keywords:
Bicycle crashCyclingData miningDecision treeFatalityInjurySafety

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Related Experiment Videos

Last Updated: Mar 7, 2026

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
07:30

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Area of Science:

  • Traffic Safety
  • Data Mining
  • Epidemiology

Background:

  • Bicycle crashes pose a significant public health risk globally.
  • Understanding factors contributing to crash severity is crucial for effective prevention strategies.
  • Official statistics provide valuable data for analyzing road safety incidents.

Purpose of the Study:

  • To identify and rank the predictors of bicycle crash severity in Italy.
  • To apply data mining techniques for analyzing crash data.
  • To inform targeted interventions for improving cyclist safety.

Main Methods:

  • An observational study using official Italian road crash statistics from 2011-2013.
  • Analysis of 49,621 crashes involving injured or killed cyclists.
  • Application of Classification and Regression Tree (CHAID) and Bayesian network analysis.

Main Results:

  • CHAID analysis identified road type, crash type, and cyclist age as primary predictors.
  • Bayesian network analysis highlighted crash type, road type, and opponent vehicle type as most influential.
  • Key factors influencing severity include road characteristics, crash dynamics, and cyclist demographics.

Conclusions:

  • Road type, crash type, and cyclist age are critical determinants of bicycle crash severity in Italy.
  • Data mining techniques effectively reveal complex relationships in crash data.
  • Findings can guide policy and infrastructure improvements to enhance cyclist safety.