No abstract

Surrogate safety measures have been recognised as suitable tools for a warning strategy. As one such measure, the time to collision (TTC) is the time remaining to a collision if the collision course and speed remain unaltered. To apply the TTC in discriminating dangerous situations, a critical threshold (TTC*) must be determined. However, a method for calculating this threshold is yet to be presented. Previously, the critical threshold has been considered as a constant value, but the value of TTC* changes the calculated probability for a crash at each time instant. In this work, a method was developed for calculating TTC* based on driver characteristics, environmental conditions, the type of preceding object and the microscopic traffic parameters of the subject vehicle based on an adaptive neuro fuzzy inference system and motion mechanics. For this purpose, data were first collected from a driving simulator. Then, to compare the collision probability calculated based on a dynamic TTC* (DTTC*) and a static TTC* (STTC*), microscopic traffic data were obtained from Modares highway in Tehran, Iran. Assessments indicated that, statistically, there is a significant difference between DTTC* and STTC* results. This finding might help to enhance the capability of in-vehicle collision-avoidance systems to prevent rear-end collisions.

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.

No abstract

A driver’s reaction time encountering hazards on roads involves different sections, and each section must occur at the right time to prevent a crash. An appropriate reaction starts with hazard detection. A hazard can be detected on time if it is completely visible to the driver. It is assumed in this paper that hazard properties such as size and color, the contrast between the environment and a hazard, whether the hazard is moving or fixed, and the presence of a warning are effective in improving driver hazard detection. A driving simulator and different scenarios on a two-lane rural road are used for assessing novice and experienced drivers’ hazard detection, and a Sugeno fuzzy model is used to analyze the data. The results show that the hazard detection ability of novice and experienced drivers decreases by 35% and 64%, respectively, during nighttime compared to daytime. Also, moving hazards increase hazard detection ability by 9% and 180% for experienced and novice drivers, respectively, compared to fixed hazards. Moreover, increasing size, contrast, and color difference affect hazard detection under nonlinear functions. The results could be helpful in safety improvement solution prioritization and in preventing vehicle-pedestrian, vehicle-animal, and vehicle-object crashes, especially for novice drivers.