Fatigued and drowsy driving: a survey of attitudes, opinions and behaviors

INTRODUCTION: There is evidence suggesting that the problem of fatigued or drowsy driving is an important contributor to road crashes. However, not much is known about public perceptions of the issue. The purpose of this study was to obtain information on attitudes, opinions, and professed practices related to fatigued or drowsy driving. METHODS: The data were gathered by means of a public opinion poll among a representative sample of 750 Ontario drivers. RESULTS: A majority of drivers (58.6%) admitted that they occasionally drive while fatigued or drowsy. Of greater importance, 14.5% of respondents admitted that they had fallen asleep or "nodded off" while driving during the past year. Nearly 2% were involved in a fatigue or drowsy driving related crash in the past year. Respondents were also asked about measures they take to overcome fatigue or drowsiness. Results indicate that relatively ineffective measures such as opening the window or playing music are the most popular; the most effective preventive measure--taking a rest--is the least popular. DISCUSSION: The prevalence of the behavior, coupled with the ineffective prevention measures favored by the public suggest there is a need for increasing their level of awareness and knowledge about the problem. IMPACT ON INDUSTRY: Results from this study further emphasize the importance of increasing the fatigued and drowsy driving knowledge base and the need to educate the public about it.     

Aggressive driving: a survey of attitudes, opinions and behaviors

INTRODUCTION: Aggressive driving encompasses a continuum of behaviors that range from extreme acts, such as shootings, to less severe manifestations, such as arguments and gestures. It is clear from the available data that aggressive driving is not uncommon and very risky. However, little is known about the opinions and practices of drivers. The purpose of this study was to help bridge these gaps. METHODS: The data were gathered by means of a public opinion poll among a representative sample of 1,201 Canadian drivers. Univariate frequency distributions and 95% confidence intervals were calculated and logistic regression and generalized linear latent models were used to summarize the data. RESULTS: It was found that the issue of aggressive driving is a significant one as a considerable percentage of drivers admits to it. The results coming from the logistic regression and the generalized linear latent model suggest that male and younger drivers are more likely to behave aggressively in traffic and that behaving more aggressively is associated with a history of traffic tickets. DISCUSSION: When gauging people's attitudes, opinions, and behaviors, it becomes clear that aggressive driving is a considerable problem. There also seems to be a need for a better understanding of which specific behaviors respondents associate with the generic term "aggressive driving." IMPACT ON INDUSTRY: Results from this study further emphasize the need of increasing the aggressive driving knowledge base.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.