TY - JOUR
T1 - Convergent validity of vision based technology (VBT) among professional bus drivers
AU - Shichrur, Rachel
AU - Ratzon, Navah Z.
N1 - Publisher Copyright:
© 2022 National Safety Council and Elsevier Ltd
PY - 2022/9
Y1 - 2022/9
N2 - Introduction: Due to the relative rarity of crashes, researchers use traffic offenses, police records, public complaints, and In-Vehicle Data Recorder (IVDR) data as proxies for assessing crash risk. In this study, a unique IVDR system, called Vision-Based Technology [(VBT), (Mobileye Inc.)] was used to monitor perilous naturalistic driving events, such as insufficient distance from other vehicles and pedestrian or bicycle rider near-misses. The study aimed to test the convergent validity of VBT as an indicator of crash involvement risk. Methods: Data from 61 professional drivers working for a large bus company were analyzed (16 of 77 in the original data cohort were excluded for insufficient VBT data). Data included: recorded VBT data, objective data collected from official records (crash records provided by the bus company, and public complaints of reckless driving), self-report data regarding crash involvement, and police tickets. The correlation between VBT, objective and self-reported data was analyzed. Binary-logistic regression modeling (BLM) was used to calculate the odds ratio (OR) for participants involved in a car crash. Results: Correlations were found between the total VBT risk score and official crash records, public complaints, and self-reports of crash involvement. The BLM correctly classified 90% of those who were involved in a crash (sensitivity) and 60% of those who were “crash-free” (specificity). The VBT total risk score was the only significant contributing factor to crash risk, and for each point of increase, the odds of being involved in a crash increased by a factor of 1.55. Conclusions: It is the first study to provide empirical evidence validating the VBT as an indicator of crash involvement and driver safety among professional bus drivers. Practical Applications: VBT technology can provide researchers and clinicians a better understanding of bus drivers' risky driving behaviors- a valuable contribution to road safety interventions for this target group.
AB - Introduction: Due to the relative rarity of crashes, researchers use traffic offenses, police records, public complaints, and In-Vehicle Data Recorder (IVDR) data as proxies for assessing crash risk. In this study, a unique IVDR system, called Vision-Based Technology [(VBT), (Mobileye Inc.)] was used to monitor perilous naturalistic driving events, such as insufficient distance from other vehicles and pedestrian or bicycle rider near-misses. The study aimed to test the convergent validity of VBT as an indicator of crash involvement risk. Methods: Data from 61 professional drivers working for a large bus company were analyzed (16 of 77 in the original data cohort were excluded for insufficient VBT data). Data included: recorded VBT data, objective data collected from official records (crash records provided by the bus company, and public complaints of reckless driving), self-report data regarding crash involvement, and police tickets. The correlation between VBT, objective and self-reported data was analyzed. Binary-logistic regression modeling (BLM) was used to calculate the odds ratio (OR) for participants involved in a car crash. Results: Correlations were found between the total VBT risk score and official crash records, public complaints, and self-reports of crash involvement. The BLM correctly classified 90% of those who were involved in a crash (sensitivity) and 60% of those who were “crash-free” (specificity). The VBT total risk score was the only significant contributing factor to crash risk, and for each point of increase, the odds of being involved in a crash increased by a factor of 1.55. Conclusions: It is the first study to provide empirical evidence validating the VBT as an indicator of crash involvement and driver safety among professional bus drivers. Practical Applications: VBT technology can provide researchers and clinicians a better understanding of bus drivers' risky driving behaviors- a valuable contribution to road safety interventions for this target group.
KW - Binary-logistic regression model
KW - Crash involvement
KW - In vehicle data recorder
KW - Naturalistic driving
KW - Professional drivers
UR - http://www.scopus.com/inward/record.url?scp=85135144751&partnerID=8YFLogxK
U2 - 10.1016/j.jsr.2022.07.007
DO - 10.1016/j.jsr.2022.07.007
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C2 - 36031270
AN - SCOPUS:85135144751
SN - 0022-4375
VL - 82
SP - 402
EP - 408
JO - Journal of Safety Research
JF - Journal of Safety Research
ER -