Road safety from the perspective of driver gender and age as related to the injury crash frequency and road scenario

Traffic Inj Prev. 2014;15(1):25-33. doi: 10.1080/15389588.2013.794943.


Objective: The objective of this research is to develop safety performance functions (SPFs) on 2-lane rural roads to predict the number of injury crashes per year per 10(8) vehicles/km on the road segment using a study on the influence of the human factors (gender, age, number of drivers) and road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash) on the effects of a crash by varying the dynamic. Countermeasures are suggested to reduce the injury crash rate and include different awareness campaigns and structural measures on the segments of road.

Methods: An 8-year period was analyzed for which 5 years of crash information were used to calibrate and specify SPFs and the remaining 3 years were used to check the reliability of the equations. Before moving to the calibration phase, a technique to filter anomalous injury crash rates was adopted by using a method widely used in geotechnical engineering that is based on estimates of ranges of values that can be considered fluctuations of the "regular" measures compared to values estimated as "abnormal" for each homogeneous scenario. Due to overdispersion of crash data, generalized estimating equations and additional log linkage equation were adopted to calibrate SPFs. The Akaike information criterion and Bayesian information criterion were used to check the reliability of the models.

Results: Six SPFs were calibrated: for head-on/side collisions, one equation was built for circular curves and one for tangent segments; for rear-end collisions, one equation was built for daylight and one for the hours of darkness; for single-vehicle run-off-road crashes, one equation was built for wet road surface conditions and one for dry road surface conditions. An original numerical variable, SLEH, was designed to calibrate safety models reflecting the identified road surface (dry/wet), light conditions (day/night), geometric element (tangent segment/circular curve), and human factors (gender/age/number drivers) all together when the crash occurred, as provided by related police reports. The validation procedure succeeded. It emerged that males and females are involved in crashes of varying degrees of frequency, depending on the driving scenario that presents itself and the gender of the other drivers involved in the crash. Several different dangerous scenarios were identified: only female drivers on a dry road surface in daylight on tangent segments increased the risk for head-on/side collisions; only male drivers on a wet road surface in daylight on circular curves increased the risk for single-vehicle crashes; and crashes involving both female and male drivers on a dry road surface in daylight on a circular curve increased the risk for head-on/side collisions.

Conclusion: According to the current study, based on the network approach for the allocation of economic resources and planning of road safety strategies, calibration of injury crash rate prediction models for specific target collision type is important because of the range of harms that are caused by different collision types. From these studies it is apparent that the age and gender of drivers considered together further refines how those factors contribute to crashes. Countermeasures (structural road interventions and/or safety awareness campaigns) can be planned to reduce the highest rate of injury crash for each gender and road scenario: the awareness campaigns cannot be generalized or vague but must be organized by age and gender, because this study shows that crash dynamics alter as these factors change, with consideration for the varying psychological traits of the driver groups. Before-and-after safety evaluations can be used to check the safety benefits of improvements carried out on the roadways, within budget constraints for improvement or safety compliance investments for future operation. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Adolescent
  • Adult
  • Age Distribution
  • Automobile Driving / psychology*
  • Automobile Driving / statistics & numerical data
  • Environment Design / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Risk Factors
  • Rural Health / statistics & numerical data*
  • Safety*
  • Sex Distribution
  • Time Factors
  • Wounds and Injuries / epidemiology*
  • Young Adult