Accident; analysis and prevention
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Randomized Controlled Trial
Naturalistic conversation improves daytime motorway driving performance under a benzodiazepine: a randomised, crossover, double-blind, placebo-controlled study.
The adverse effects of benzodiazepines on driving are widely recognised. The aims of this study were both to determine the impact of naturalistic conversation on the driving ability of drivers under a benzodiazepine, and to measure the accuracy of drivers' assessments of the joint effects of the benzodiazepine and conversation. Sixteen healthy male participants (29.69 ± 3.30 years) underwent a randomised, crossover, double-blind, placebo-controlled study with the benzodiazepine lorazepam (2mg). ⋯ Pearson's correlation coefficients revealed that self-assessments were (i) not at all predictive of lane-keeping when performed before the drive, but (ii) moderately predictive of lane-keeping performance when performed during or after the drive. We conclude that conversation with a passenger may contribute to safer lane-keeping when driving under a benzodiazepine. Moreover, a degree of awareness may be attained after some experience of driving under the influence of this type of medication.
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This study proposes a Bayesian spatial joint model of crash prediction including both road segments and intersections located in an urban road network, through which the spatial correlations between heterogeneous types of entities could be considered. A road network in Hillsborough, Florida, with crash, road, and traffic characteristics data for a three-year period was selected in order to compare the proposed joint model with three site-level crash prediction models, that is, the Poisson, negative binomial (NB), and conditional autoregressive (CAR) models. According to the results, the CAR and Joint models outperform the Poisson and NB models in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-entity spatial correlations. Although the goodness-of-fit and predictive performance of the CAR and Joint models are equivalent in this case study, spatial correlations between segments and the connected intersections are found to be more significant than those solely between segments or between intersections, which supports the employment of the Joint model as an alternative in road-network-level safety modeling.
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Over a period of five years, blood samples were taken from 1046 drivers killed as a result of a motor vehicle crash on New Zealand roads. These were analysed for the presence of alcohol and a range of both illicit drugs and psychoactive medicinal drugs. Driver culpability was determined for all crashes. ⋯ In this study, there were very few drivers who had used a single drug, other than cannabis or alcohol. Therefore, from this study, it is not possible to comment on any relationship between opioid, stimulant or sedative drug use and an increased risk of being killed in a crash for the drivers using these drugs. The results from a multivariate analysis indicate that driver gender, age group and licence status, (P=0.022, P=0.016, P=0.026, respectively), the type of vehicle being driven (P=0.013), the number of vehicles in the crash (P<0.001), the blood alcohol concentration of the driver (P<0.001) and the use of any drug other than alcohol and cannabis (P=0.044), are all independently associated with culpability.