Accident; analysis and prevention
-
We developed a hazard perception test, modeled on that used currently in several Australian states, that presents short video scenes to observers and requires them to indicate the presence of a traffic conflict that would lead to a collision between the "camera" vehicle and another road user. After eliminating those scenes that were problematic (e.g., many observers did not recognize the hazard), we predicted driver group (novice vs. experienced drivers of similar age) on the basis of individual differences in reaction time, miss rate and false alarm rate. Novices were significantly slower in responding to hazards, even after controlling for age and simple reaction time. ⋯ There was good reliability in the resulting scale. Results suggest that this brief test of hazard perception can discriminate groups that differ in driving experience. Implications for driver licensing, evaluation and training are discussed.
-
Road crashes not only claim lives and inflict injuries but also create an economic burden to the society due to loss of productivity. Although numerous studies have been conducted to examine a multitude of factors contributing to the frequency and severity of crashes, very few studies have examined the influence of street pattern at a community level. This study examined the effect of different street patterns on crash severity using the City of Calgary as a case study. ⋯ Their effects on injury risk are examined together with other factors including road features, drivers' characteristics, crash characteristics, environmental conditions and vehicle attributes. Pedestrian and bicycle crash data for the years 2003-2005 were utilized to develop a multinomial logit model of crash severity. Our results showed that compared to other street patterns, loops and lollipops design increases the probability of an injury but reduces the probability of fatality and property-damage-only in an event of a crash.
-
The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. ⋯ Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25mile, 0.5mile and 1mile buffer widths to extract data and develop models was also evaluated.
-
Comparative Study
Nilsson's Power Model connecting speed and road trauma: applicability by road type and alternative models for urban roads.
Nilsson (1981) proposed power relationships connecting changes in traffic speeds with changes in road crashes at various levels of injury severity. Increases in fatal crashes are related to the 4(th) power of the increase in mean speed, increases in serious casualty crashes (those involving death or serious injury) according to the 3(rd) power, and increases in casualty crashes (those involving death or any injury) according to the 2(nd) power. Increases in numbers of crash victims at cumulative levels of injury severity are related to the crash increases plus higher powers predicting the number of victims per crash. ⋯ The estimated power applicable to serious casualties on urban arterial roads was significantly less than that on rural highways, which was also significantly less than that on freeways. Alternative models linking the parameters of speed distributions with road trauma are reviewed and some conclusions reached for their use on urban roads instead of Nilsson's model. Further research is needed on the relationships between serious road trauma and urban speeds.
-
Controlled Clinical Trial
The effect of alcohol, THC and their combination on perceived effects, willingness to drive and performance of driving and non-driving tasks.
Driving under the influence of drugs (DUID) is one of the main causes of car accidents. Alcohol and marijuana are the most popular drugs among recreational users. Many classify these drugs as "Light" drugs and therefore allow themselves to drive after consuming them. ⋯ Overall, the combination of alcohol and THC had the most intense effect after intake. This effect was reflected in performance impairments observed in the driving and non-driving tasks, in the subjective sensations after intake, and in the physiological measures. Despite significant differences in the size of the effects after the various treatments, there were no differences in the distances subjects were willing to drive while under the influence on each of the treatments.