Influence of red-light violation warning systems on driver behavior – a driving simulator study

Abstract

Objective: With the advent of connected and automated vehicle (CAV) technology, there is an increasing need to evaluate driver behavior with the advent of connected and automated vehicle (CAV) technology. This study introduced a red-light violation warning (RLVW) system using CAV technology in a driving simulator environment, to evaluate driver braking behavior when the light changes suddenly from green to yellow.

Methods: Researchers recruited a total of 93 participants from diverse socio-economic backgrounds for this study and created a virtual network of downtown Baltimore. An eye tracking device was used to observe distractions and head movements. A Lognormal accelerated failure time (AFT) distribution model was used for this analysis, to calculate speed reduction times from the moment the traffic light changes from green to yellow, to the point where a minimum speed was reached.

Results: It was observed that speed reduction times were significantly higher in the presence of a RLVW system, requiring a longer period of time to come to a complete stop at the red light. Inferences can be drawn from the jerk analysis that, the RLVW system results in a highly unsafe jerk at the onset of the warning. Without the RLVW system though, a highly uncomfortable positive jerk occurs closer to the signal, which is due to sudden acceleration, as the participants possibly slowed down a lot initially. Gaze analysis showed that the system was able to attract the attention of the drivers, as the majority of the drivers noticed the displayed warning.

Conclusions: The findings suggest that the presence of an RLVW system sends a clear message to the driver about the change in traffic light and gives the driver ample time to adapt their initial approach speed to stop at the signal, avoiding potential intersection crashes.

Keywords:

• Connected vehicles
• red-light violation warning
• driving simulator
• accelerated failure time distribution
• eye tracking system
• gaze analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgments

This study was supported by the Urban Mobility and Equity Center, a Tier 1 University Transportation Center of the U.S. DOT University Transportation Centers Program at Morgan State University. The authors would like to acknowledge the guidance and support received from Dr. Young-Jae Lee, Associate Professor at Morgan State University.

Additional information

Funding

This study was funded (Grant# 69A43551747123) by the Urban Mobility and Equity Center, a Tier 1 University Transportation Center of the U.S. DOT University Transportation Centers Program at Morgan State University.