Predictors Of Crash Involvement Research Articles

Latent Classes of Polydrug Users as a Predictor of Crash Involvement and Alcohol Consumption.

Polydrug users have been shown to be at higher risk for alcohol consumption and crash involvement. However, research has shown that polydrug groups differ in some important ways. It is currently unknown how polydrug-using groups differ in terms of crash involvement and alcohol consumption. The current study used latent class analysis to examine subgroups of polydrug users (n = 384) among a sample of drivers in Virginia Beach, Virginia (N = 10,512). A series of logistic regression analyses were conducted to determine the relationship between polydrug use categories and crash involvement and alcohol consumption. Four distinct subclasses of users were identified among polydrug-using drivers: Class 1 is the "marijuana-amphetamines class" and accounts for 21.6% of polydrug users. Class 2 is the "benzo-antidepressant class" and accounts for 39.0% of polydrug users. Class 3 is the "opioid-benzo class" and accounts for 32.7% of polydrug users. Finally, Class 4 is the "marijuana-cocaine class" and accounts for 6.7% of the study sample. Drivers in the opioid-benzo class were significantly more likely than those in any other class as well as non-drug users and single-drug users to be involved in a crash and were more likely than those in most other conditions to consume alcohol. No significant difference was found between marijuana-amphetamine users or benzo-antidepressant users and non-drug users on crash risk. Some polydrug users are indeed at greater risk for crash involvement and alcohol consumption; however, not all polydrug users are significantly worse than single-drug users and/or non-drug users, and the practice of lumping polydrug users together when predicting crash risk runs the risk of inaccurately attributing crash involvement to certain drivers.

The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China

Although the taxi industry is playing an important role in Chinese everyday life, little attention has been posed towards occupational health issues concerning the taxi drivers’ working conditions, driving behaviour and road safety. A cross-sectional survey was administered to 1021 taxi drivers from 21 companies in four Chinese cities and collected information about (i) sociodemographic characteristics, (ii) working conditions, (iii) frequency of daily aberrant driving behaviour, and (iv) involvement in property-damage-only (PDO) and personal injury (PI) crashes over the past two years. A hybrid bivariate model of crash involvement was specified: (i) the hybrid part concerned a latent variable model capturing unobserved traits of the taxi drivers; (ii) the bivariate part modelled jointly both types of crashes while capturing unobserved correlation between error terms. The survey answers paint a gloomy picture in terms of workload, as taxi drivers reported averages of 9.4 working hours per day and 6.7 working days per week that amount on average to about 63.0 working hours per week. Moreover, the estimates of the hybrid bivariate model reveal that increasing levels of fatigue, reckless behaviour and aggressive behaviour are positively related to a higher propensity of crash involvement. Lastly, the heavy workload is also positively correlated with the higher propensity of crashing, not only directly as a predictor of crash involvement, but also indirectly as a covariate of fatigue and aberrant driving behaviour. The findings from this study provide insights into potential strategies for preventive education and taxi industry management to improve the working conditions and hence reduce fatigue and road risk for the taxi drivers.

Isolating Risk Factors for Crash Frequency among Older Drivers

By the year 2024, 25% of drivers in the U.S. will be over the age of 65. Older drivers have more crashes and fatalities per mile driven than any other adult age group. Although driving is a highly visual task and vision impairment is more prevalent in the elderly, previous research has failed to identify visual factors which are strongly associated with increased crashes in the elderly. Using a comprehensive approach to assess several aspects of visual processing in a large sample of older drivers, this study has identified a measure of visual attention that had high sensitivity (89%) and specificity (85%) in predicting which older drivers had a history of crash problems, a level of predictability unprecedented in research on crash risk in older drivers. The “useful field of view”, as it is called, measures the spatial area within which an individual can be rapidly alerted to visual stimuli. Older adults with substantial shrinkage in the useful field of view were six times more likely to have incurred one or more crashes in the previous five year period. By comparison, visual sensory function, cognitive status, and chronological age were poor predictors of crash involvement. This study suggests that policies which restrict driving privileges based solely on age or on stereotypes of age-related declines in vision and cognition are scientifically unfounded. With the identification of a visual attention measure highly predictive of crash problems in the elderly, decisions on the suitability of licensure in the older adult population can be based on objective, visual-performance-based criteria.