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 Experiment Videos

Predicting real-time traffic conflicts using deep learning.

Nicolette Formosa1, Mohammed Quddus1, Stephen Ison2

  • 1School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom.

Accident; Analysis and Prevention
|January 14, 2020
PubMed
Summary

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

394
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
394
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

20.0K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
20.0K

You might also read

Related Articles

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

Sort by
Same author

Association of a standardized nursing protocol with postoperative complications following open radical gastrectomy: a retrospective cohort study.

Open medicine (Warsaw, Poland)·2026
Same author

Comparative safety evaluation of ADAS-Equipped electric and gasoline vehicles using real-world crash data.

Accident; analysis and prevention·2026
Same author

Evaluation of the safety improvement effects and adaptability of speed limit measures on downhill curved sections of mountainous freeways.

Accident; analysis and prevention·2026
Same author

When does visual distraction become dangerous in car-following? Evidence from naturalistic driving study data with causal inference on time-to-collision and braking intensity.

Accident; analysis and prevention·2026
Same author

Determinants influencing risks in e-bike cyclists under mix traffic condition: a partially constrained random parameters approach using experimental study data.

Accident; analysis and prevention·2025
Same author

Segment level safety analysis using lane-changing behavior and driving volatility features from connected vehicle trajectories.

Scientific reports·2025

This study introduces a novel deep learning approach for real-time traffic conflict prediction, improving Advanced Driver-Assistance Systems (ADAS) safety. The developed model achieves 94% accuracy by integrating diverse data, enhancing proactive collision mitigation strategies.

Failed At:

2026-06-19T13:38:26.018817+00:00

Keywords:
Deep Neural Network (DNN)Regional–Convolution Neural Network (R-CNN)Safety Surrogate Measuresdata integration architecturetraffic conflicts

Related Experiment Videos