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

Large-scale transportation governance: A tensorization-parallelization co-empowered framework.

Innovation (Cambridge (Mass.))·2026
Same author

Exploring the effect of driver drowsiness on takeover performance during automated driving: An updated literature review.

Accident; analysis and prevention·2025
Same author

Factors influencing behavioral intentions to use conditionally automated vehicles.

Journal of safety research·2025
Same author

Naturalistic driving study data applied to road infrastructure: A systematic review.

Journal of safety research·2025
Same author

The role of safety in modal choice and shift: A transport expert perspective in the state of Victoria (Australia).

PloS one·2023
Same author

Liraglutide inhibits AngII-induced cardiac fibroblast proliferation and ECM deposition through regulating miR-21/PTEN/PI3K pathway.

Cell and tissue banking·2022

Related Experiment Video

Updated: Jul 24, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.9K

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach.

Zheng Xu1, Nan Zheng1, David B Logan2

  • 1Institute of Transport Studies, Monash University, Clayton, VIC 3800, Australia.

Accident; Analysis and Prevention
|July 4, 2023
PubMed
Summary
This summary is machine-generated.

New simulation methods generate realistic cyclist-driver conflict data, revealing vehicle acceleration as a key conflict cause. This approach improves understanding beyond traditional crash data analysis.

Keywords:
Bicycle-vehicle conflictsData generationSurrogate safety measurementsVirtual reality

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

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

Published on: February 1, 2020

20.4K

Related Experiment Videos

Last Updated: Jul 24, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.9K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

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

Published on: February 1, 2020

20.4K

Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Human Factors in Transportation

Background:

  • Urban road networks experience significant animosity and conflicts between drivers and cyclists, particularly in shared spaces.
  • Existing conflict assessment methods rely on statistical analysis with limited data, and actual crash data is sparse and insufficient for detailed analysis.

Purpose of the Study:

  • To propose and validate a novel simulation-based approach for generating and assessing bicycle-vehicle conflict data.
  • To overcome limitations of sparse real-world data and enhance understanding of cyclist-driver interactions.

Main Methods:

  • Utilized a 3D visualization and virtual reality platform integrated with traffic microsimulation to create a naturalistic driving/cycling environment.
  • Validated the simulation platform for accurately reflecting human-like driving and cycling behaviors across various infrastructure designs.
  • Conducted comparative experiments on bicycle-vehicle interactions across 960 scenarios, analyzing data with a surrogate safety assessment model (SSAM).

Main Results:

  • Identified that high conflict probability scenarios do not always result in actual crashes, questioning the sufficiency of traditional SSAM metrics (TTC, PET).
  • Determined that variations in vehicle acceleration are the primary cause of conflicts, implicating drivers as the main responsible party.
  • Demonstrated the approach's capability to generate near-miss events and replicate cyclist-driver interaction patterns.

Conclusions:

  • The simulation-based approach effectively generates valuable near-miss data for cyclist-driver interactions, overcoming real-world data limitations.
  • Findings suggest that traditional safety metrics may not fully capture the nuances of cyclist-driver conflicts.
  • Vehicle acceleration variability is a critical factor in cyclist-driver conflicts, highlighting the need for driver awareness and potentially infrastructure adjustments.