Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Determination of Expected Frequency

Determination of Expected Frequency

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...

Hindsight Biases

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

Causality in Epidemiology

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

You might also read

Related Articles

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

Sort by

Same author

AI revolution in insurance: bridging research and reality.

Frontiers in artificial intelligence·2025

Same author

Lowering the legal alcohol limits for driving: expected changes in the prevalence of positive tests.

Gaceta sanitaria·2025

Same author

Analysis of the surprise question as a tool for predicting death in neonates.

European journal of pediatrics·2025

Same author

Cross-sectional study of the perceived wellbeing of children in palliative care.

Anales de pediatria·2025

Same author

Suffering in children and adolescents in paediatric palliative care in Spain: psychometric properties of the qESNA scale.

Anales de pediatria·2024

Same author

Pricing weekly motor insurance drivers' with behavioral and contextual telematics data.

Heliyon·2024

Search research articles

Related Experiment Video

Updated: Aug 8, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence.

Leandro Masello¹, German Castignani², Barry Sheehan³

¹University of Limerick, Limerick KB3-040, Ireland; Motion-S S.A., Mondorf-les-Bains L-5610, Luxembourg.

Accident; Analysis and Prevention

|February 28, 2023

Summary

This summary is machine-generated.

Driving context significantly predicts accident risk. Factors like speed limits, weather, and road conditions influence speeding, distraction, and harsh maneuvers, aiding insurers and safety experts.

Keywords:

Driving context Explainable AI Machine learning Risk assessment Usage-based insurance

More Related Videos

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

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

Published on: December 18, 2020

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Related Experiment Videos

Last Updated: Aug 8, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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

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

Published on: December 18, 2020

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Area of Science:

Data Science
Transportation Safety
Insurance Telematics

Background:

Usage-based insurance leverages driving behavior data for premium adjustment.
Telematics data offers insights into driving contexts (road type, weather, traffic).
Driving contexts significantly influence accident exposure and risk.

Purpose of the Study:

Investigate the relationship between driving context combinations and driving risk.
Identify and rank contextual factors predicting near-misses, speeding, and distraction events.
Provide insights for road safety stakeholders and insurers.

Main Methods:

Utilized a naturalistic driving dataset (77,859 km).
Employed XGBoost and Random Forests for predictive modeling.
Applied Shapley Additive Explanations to identify and rank feature importance.

Main Results:

Driving context is a significant predictor of driving risk.
Key predictors include speed limit, temperature, wind speed, traffic, and road slope.
Low speed limits increase speeding events; low temperatures decrease harsh maneuvers; precipitation increases harsh maneuvers and distractions.

Conclusions:

Driving context analysis enhances road safety and insurance risk assessment.
Specific contextual factors have predictable impacts on various risky driving events.
Methodology supports data-driven strategies for mitigating road accidents.