Accident Analysis & Prevention 

Abstract: Driving speed is an important factor in road safety. Speed not only affects the severity of a crash, but is also related to the risk of being involved in a crash. This paper discusses the most important empirical studies into speed and crash rate with an emphasis on the more recent studies. The majority of these studies looked at absolute speed, either at individual vehicle level or at road section level. Respectively, they found evidence for an exponential function and a power function between speed and crash rate. Both types of studies found evidence that crash rate increases faster with an increase in speed on minor roads than on major roads. At a more detailed level, lane width, junction density, and traffic flow were found to interact with the speed-crash rate relation. Other studies looked at speed dispersion and found evidence that this is also an important factor in determining crash rate. Larger differences in speed between vehicles are related to a higher crash rate. Without exception, a vehicle that moved (much) faster than other traffic around it, had a higher crash rate. With regard to the rate of a (much) slower moving vehicle, the evidence is inconclusive.

Abstract: Looking back over 30 years of my own and other safety-climate scholars' research, my primary reflection is that we have achieved an enormous task of validating safety climate as a robust leading indicator or predictor of safety outcomes across industries and countries. The time has therefore come for moving to the next phase of scientific inquiry in which constructs are being augmented by testing its relationships with antecedents, moderators and mediators, as well as relationships with other established constructs. Whereas there has been some significant progress in this direction over the last 30 years (e.g. leadership as a climate antecedent), much more work is required for augmenting safety climate theory. I hope this article will stimulate further work along these lines.

Abstract: The relationship between sensation seeking and risky behaviour has been observed since the 1970s. During the late 1980s and early 1990s, road safety researchers have examined the relationship between sensation seeking and risky driving (e.g. driving while impaired, speeding, following too closely), as well as its consequences (e.g. collisions, violations). There is also growing evidence that sensation seeking may also moderate the manner in which drivers respond to other factors such as alcohol impairment and perceived risk. This paper reviews and synthesizes the literature on sensation seeking as a direct influencer of risky driving and its consequences and as a moderator of the influence of other factors. The vast majority of the 40 studies reviewed showed positive relationships between sensation seeking (SS) and risky driving, with correlations in the 0.30-0.40 range, depending on gender and the measure of risky driving and SS employed. Of those studies that have looked at the subscales of Zuckerman's Sensation Seeking Scale, Thrill and Adventure Seeking appears to have the strongest relationship to risky driving. The biological bases of SS is discussed as are the implications for collision prevention measures.

Abstract: The Negative Binomial modeling technique was used to model the frequency of accident occurrence and involvement. Accident data over a period of 3 years, accounting for 1,606 accidents on a principal arterial in Central Florida, were used to estimate the model. The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the section's length, on the frequency of accident occurrence. Several Negative Binomial models of the frequency of accident involvement were also developed to account for the demographic characteristics of the driver (age and gender). The results showed that heavy traffic volume, speeding, narrow lane width, larger number of lanes, urban roadway sections, narrow shoulder width and reduced median width increase the likelihood for accident involvement. Subsequent elasticity computations identified the relative importance of the variables included in the models. Female drivers experience more accidents than male drivers in heavy traffic volume, reduced median width, narrow lane width, and larger number of lanes. Male drivers have greater tendency to be involved in traffic accidents while speeding. The models also indicated that young and older drivers experience more accidents than middle aged drivers in heavy traffic volume, and reduced shoulder and median widths. Younger drivers have a greater tendency of being involved in accidents on roadway curves and while speeding.

Abstract: This paper evaluates the performance of Poisson and negative binomial (NB) regression models in establishing the relationship between truck accidents and geometric design of road sections. Three types of models are considered: Poisson regression, zero-inflated Poisson (ZIP) regression, and NB regression. Maximum likelihood (ML) method is used to estimate the unknown parameters of these models. Two other feasible estimators for estimating the dispersion parameter in the NB regression model are also examined: a moment estimator and a regression-based estimator. These models and estimators are evaluated based on their (i) estimated regression parameters, (ii) overall goodness-of-fit, (iii) estimated relative frequency of truck accident involvements across road sections, (iv) sensitivity to the inclusion of short road sections, and (v) estimated total number of truck accident involvements. Data from the Highway Safety Information System are employed to examine the performance of these models in developing such relationships. The evaluation results suggest that the NB regression model estimated using the moment and regression-based methods should be used with caution. Also, under the ML method, the estimated regression parameters from all three models are quite consistent and no particular model outperforms the other two models in terms of the estimated relative frequencies of truck accident involvements across road sections. It is recommended that the Poisson regression model be used as an initial model for developing the relationship. If the overdispersion of accident data is found to be moderate or high, both the NB and ZIP regression models could be explored. Overall, the ZIP regression model appears to be a serious candidate model when data exhibit excess zeros, e.g. due to underreporting. However, the interpretation of the ZIP model can be difficult.