Accident; analysis and prevention
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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.
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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.
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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.