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

You might also read

Related Articles

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

Sort by
Same author

The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model.

International journal of environmental research and public health·2023
Same author

Traffic flow prediction using bi-directional gated recurrent unit method.

Urban informatics·2022
Same author

Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China.

International journal of environmental research and public health·2022
Same author

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China.

International journal of environmental research and public health·2022
Same author

Resident travel mode prediction model in Beijing metropolitan area.

PloS one·2021
Same author

Supplementary data for the mechanism research for depolymerization of cellulose induced by hydroxyl radical using GC-MS, reaction kinetics simulation and quantum chemistry computation.

Data in brief·2020
Same journal

Correction: Grewal et al. Diversity and Representation in Cardiovascular Research: Evidence Gaps, Emerging Models, and Policy Implications. <i>Int. J. Environ. Res. Public Health</i> 2026, <i>23</i>, 241.

International journal of environmental research and public health·2026
Same journal

Drinking Water Quality and Health Risk Assessment in Rural Ghana: Evidence from North-East and North Gonja Districts in the Savannah Region.

International journal of environmental research and public health·2026
Same journal

Physical Activity of University Students During COVID-19 Restrictions: Evidence from Poland.

International journal of environmental research and public health·2026
Same journal

Assessment of Occupational Health and Safety Hazards in Mosquito Control Personnel in North Carolina and Virginia, USA.

International journal of environmental research and public health·2026
Same journal

Association Between Dysfunctional Parenting Practices and Suspected Gaming Disorder Among Japanese Male Junior High School Students: A Cross-Sectional Study of Parental Assessment.

International journal of environmental research and public health·2026
Same journal

A National Virtual Peer Support Group for Women Veterans Living with Breast Cancer: Lessons from the Field.

International journal of environmental research and public health·2026
See all related articles

Related Experiment Video

Updated: Mar 10, 2026

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

5.1K

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance.

Juan Li1, Xudong Jia2, Chunfu Shao3

  • 1MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. juanli@bjtu.edu.cn.

International Journal of Environmental Research and Public Health
|December 9, 2016
PubMed
Summary
This summary is machine-generated.

Drivers’ decisions at yellow lights can be predicted. Vehicle distance to the stop line is key for predicting stop/go decisions and red light running (RLR) violations, improving traffic safety.

Keywords:
driver behaviorsequential logit modelsignalized intersectionvideo surveillance

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

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

Published on: September 18, 2012

17.9K

Related Experiment Videos

Last Updated: Mar 10, 2026

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

5.1K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

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

Published on: September 18, 2012

17.9K

Area of Science:

  • Traffic Engineering
  • Transportation Safety
  • Driver Behavior Analysis

Background:

  • Signalized intersections pose complex decision-making challenges for drivers at yellow light onset.
  • Incorrect stop/go decisions during yellow intervals contribute to red light running (RLR) violations and crashes.
  • Predicting driver behavior is crucial for enhancing traffic safety at intersections.

Purpose of the Study:

  • To develop a predictive model for driver stop/go decisions at yellow signals.
  • To identify key factors influencing red light running (RLR) violations.
  • To enhance traffic safety through better understanding of driver behavior.

Main Methods:

  • Utilized a Vehicle Data Collection System (VDCS) to gather traffic data including speed, acceleration, and distance.
  • Employed an enhanced Gaussian Mixture Model (GMM) for vehicle extraction and Kalman Filter (KF) for trajectory acquisition.
  • Applied a sequential logit model to analyze driver decisions and RLR violations.

Main Results:

  • Vehicle distance to the stop line at yellow onset significantly predicts both stop/go decisions and RLR violations.
  • Approaching vehicle speed is a notable factor influencing stop/go decisions.
  • Post-yellow onset vehicle acceleration correlates positively with RLR violations.

Conclusions:

  • Driver stop/go decisions and RLR violations are predictable based on real-time traffic parameters.
  • Distance to the stop line and vehicle dynamics are critical indicators for predicting risky driving behavior.
  • Findings can inform interventions to reduce RLR violations and improve intersection safety.