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

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

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.

Rolling Resistance: Problem Solving

Rolling Resistance: Problem Solving

Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...

Observational Studies

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...

You might also read

Related Articles

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

Sort by

Same author

Projecting airborne tire wear particle emissions in the United States in the era of electric vehicles.

The Science of the total environment·2025

Same author

Safety and immunogenicity of an inactivated SARS-CoV-2 vaccine (FAKHRAVAC®) in healthy adults aged 18-55 years: Randomized, double-blind, placebo-controlled, phase I clinical trial.

Vaccine: X·2023

Same author

Phase II randomized, double blind, placebo controlled, clinical trial of safety and immunogenicity of an inactivated SARS-CoV-2 vaccine FAKHRAVAC in adults aged 18-70 years.

BMC infectious diseases·2023

Same author

Impact of an exclusive narrow automated vehicle lane on adjacent lane driver behavior.

Accident; analysis and prevention·2022

Same author

Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally.

International journal of environmental research and public health·2022

Same author

Investigating the impact of driving automation systems on distracted driving behaviors.

Accident; analysis and prevention·2021

Same journal

Modeling road-segment-level speeding risk of new energy vehicle taxis using a multistage framework with spatial spillover, endogeneity, and nonlinear effects.

Accident; analysis and prevention·2026

Same journal

Role of streetscape feature in pedestrian safety: A modified multi-level multiple membership model.

Accident; analysis and prevention·2026

Same journal

Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.

Accident; analysis and prevention·2026

Same journal

Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

Accident; analysis and prevention·2026

Same journal

Differential sensitivity of self-reported driving and collision measures to aspects of shiftwork, sleep, and fatigue.

Accident; analysis and prevention·2026

Same journal

Delving into the visual attention of pedestrians during street crossing under time pressure: An eye-tracking approach.

Accident; analysis and prevention·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 18, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Red-light running violation prediction using observational and simulator data.

Arash Jahangiri¹, Hesham Rakha¹, Thomas A Dingus²

¹Center for Sustainable Mobility, Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States.

Accident; Analysis and Prevention

|July 4, 2016

Summary

This summary is machine-generated.

Red-light running (RLR) prediction models can be developed using machine learning. Key factors for predicting RLR violations include time to intersection, distance to intersection, and velocity at yellow onset.

Keywords:

Driver violation Machine learning Observational data Random forest Red-light running Signalized intersection Simulator data Violation prediction

More Related Videos

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Related Experiment Videos

Last Updated: Mar 18, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Area of Science:

Traffic Safety Engineering
Machine Learning Applications
Transportation Data Analysis

Background:

Red-light running (RLR) is a significant cause of traffic accidents, resulting in numerous fatalities and injuries annually.
Identifying potential RLR violations before they occur is crucial for implementing preventative measures for road users and infrastructure.
Existing methods for RLR violation detection often lack predictive capabilities.

Purpose of the Study:

To assess the feasibility of developing predictive models for red-light running (RLR) violations.
To compare the effectiveness of different data sets (observational vs. driver simulator) in predicting RLR violations.
To identify the most influential factors for RLR violation prediction using machine learning.

Main Methods:

Utilized two distinct datasets: observational vehicle data and driver simulator data, each with unique contributing factors.
Employed the random forest (RF) machine-learning technique to build and evaluate RLR violation prediction models.
Conducted a sensitivity analysis to determine factor importance and the impact of different time frames on model performance.

Main Results:

Both observational and simulator data models identified Time to Intersection (TTI), Distance to Intersection (DTI), Required Deceleration Parameter (RDP), and velocity at yellow onset as key predictive factors.
Factor importance varied depending on the data set and time frames used for model development.
Monitoring periods of 2-6 meters before the intersection significantly improved RLR prediction model performance.

Conclusions:

Predictive models for red-light running violations can be effectively developed using machine learning techniques.
A combination of vehicle kinematic data and driver-specific factors (when available) can enhance prediction accuracy.
Utilizing data from defined monitoring periods, in addition to point-in-time data, provides valuable insights for proactive traffic safety interventions.