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Utilizing crash and violation data to assess unsafe driving actions

Rezapour Mashhadi M. M., Wulff S. S., Ksaibati K.

Journal of Sustainable Development of Transport and Logistics

2017

Vol. 2, No. 2

35--46

Wyoming has one of the highest crash rates in the United States and a higher fatality rate than the U.S. average. These high rates result from many factors such as the high traffic through I-80 and the mountainous areas of Wyoming. This study employed two approaches to study contributory factors to crashes in the most hazardous interstate, I-80, in Wyoming by employing crash and citation data sets. Different factors may contribute to different driver actions so it is important to consider these crash causes separately. Thus, multiple logistic regression models were used in this study to examine the differences in crash-contributing factors for three driver actions: driving too fast for conditions, improper lane change, and no improper driving. These driver actions account for about 70% of all the crash causes on this interstate. The same violations as the two driver actions, improper lane change and driving too fast for conditions, account for 42% of all the crashes. The literature has indicated that previous violations can be used to predict future violations, and consequently crashes. Therefore, these violations were identified to detect the groups that are at higher risk of involvement in crashes. The analyses indicated that there are substantial differences across different driver actions for crash and violation data. For instance, not-dry-surface conditions increased the estimated odds of driving too fast for conditions 33 times while it decreased the risk of no improper driving by an estimated 250%. Crash severity, number of vehicles, vehicle maneuver, point of impact, driver condition, and speed compliance also impacted different driver actions differently. The results of violation analyses revealed that the interaction between types of vehicle and various variables were significant. For instance, nonresident truck drivers were more likely to violate all types of risky violations, which increased the estimated odds of crashes, compared with resident truck drivers. Recommendations based on the results are provided for policy makers to reduce high crash rate in the state.