Abstract
Road safety has recently been considered an important issue in the country. Single-vehicle accident statistics show the importance of this issue. From a safety viewpoint, drivers need to have a reasonable time window for hazard recognition and reaction; therefore, the hazard has to be in sight from a distance preferably longer than the standard minimum stopping sight distance. Nevertheless, if the roadside configuration makes the sight available for a very long distance, the hazard properties are the ones defining the visibility. The hazard size, color, and mobility are some of the most important hazard properties, which may mainly interact with ambient light (like being day or night) and driving speed. In this research, effect of hazard properties on driving accident likelihood was investigated in a condition that enough recognition and reaction time window was available for the driver to provide a ceteris paribus experiment. To fulfil that in a safe experiment condition, a driving simulator was used to test the behavior of 90 licensed drivers encountering an average of 14 hazards with various sets of properties. Based on the findings of this research, there are some interactions between influential hazard properties. The results imply that it is approximately 23% more likely to observe an accident when encountering a dark small stationary hazard at nighttime like a dark-colored with an observed size of 0.5 m × 0.5 m (e.g., a stone) than a major moving light-colored hazard in the daytime like a camel of 1.5 m ∗ 2 m in size. A green-colored hazard is 27% less likely to involve in an accident at nighttime than hazards with other colors. Each 10 km/h speed increment leads to 1.9% more accident likelihood, and every time the driver encounters a hazard, they will be 0.84% less likely to crash next time.
Highlights
Road safety has recently been considered an important issue in the country
E fourth Gauss–Markov assumption, no multicollinearity, is investigated using the variance inflation factor (VIF). e estimated VIFs for this model corresponding to each variable are all less than 10; there is no multicollinearity in the model, and the 4th assumption is confirmed (see Table A-5 in the Appendix (Supplementary Materials))
Since the observed dependent variable in the Linear Probability Model (LPM) model is a binary variable, the error term in the calculation will be divided into two groups of success and failure observations, and the mean of each group shows a difference of 1 unit. is condition leads to a positive heteroskedasticity test outcome even if there is no varying variance
Summary
Road safety has recently been considered an important issue in the country. Single-vehicle accident statistics show the importance of this issue. According to the NCHRP Report 600 regarding the human factors in highway systems, influential factors on PRT include color contrast between the hazard and the environment, light glare, the driver’s anticipations, road visual complexity, drivers’ experience and familiarity with the road, the driver’s age, and complexity of the hazardous situation [5]. These factors are deemed to affect all types of hazardous situations in roads, including non-vehicular hazards, little is known about non-vehicular hazards themselves. The influence of the characteristics of a non-vehicular not-anticipated hazard and how these characteristics have interaction still need to be investigated. us, in this study, we aim to pay a more precise attention to these hazards and their characteristics
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