Citation

Jashami H, Hurwitz D, Monsere C, Kothuri S. Adv. Transp. Stud. 2020; (SI 2): 143-156.

Copyright

(Copyright © 2020, Arcane Publishers)

DOI

unavailable

PMID

unavailable

Abstract

In 2016, the U.S. recorded the highest number of pedestrian fatalities since 1990. Turning vehicles pose a collision risk for non-motorized road users. To improve traffic safety and efficiency at signalized intersections, driver behavior associated with right-of-way transitions at signalized intersections must be understood more comprehensively. This study explored the safety concern of driver's potential to incorrectly interpret the solid circular green (SCG) during right-turns using a high-fidelity driving simulator. A counter-balanced, factorial design was chosen to explore two independent variables: signal indication type and presence of a pedestrian. The pre-turn speed, visual attention and driver decision making were used as performance measures. Data were obtained from 46 participants (21 women) turning right 184 times in 4 experimental scenarios. Two linear mixed effects models and a frequency analysis were used to examine within-subject variables on observed performance.

RESULTS from both the frequency analysis and the statistical model suggest that for the same turning maneuver, drivers presented a SCG were less likely to exhibit correct behavior. While drivers had similar speed for both the SCG and solid green arrow (SGA) signals, drivers fixated on the SGA head longer. The similar speed indicates that drivers are interpreting the SCG as a protected indication. When presented with the SCG indication in the presence of pedestrians, 33% of drivers exhibited improper behavior while turning right, resulting in a situation with high crash potential. For the same turning maneuver, drivers presented with SGA indication were more likely to exhibit correct behavior. This indicates that SGA can promote a safer interaction between right turning vehicles and pedestrians in the conflicting crosswalk. These findings provide quantitative data that could be used by transportation agencies to improve driver comprehension and pedestrian safety at signalized intersections.

Language: en

Keywords

Crashes; Driver; Road Safety