Safety performance analysis of horizontal curves in urban areas.

While numerous studies have examined horizontal curve risk factors in rural areas, there is only one study for urban areas. Moreover, previous studies have used limited datasets, which tend to generate an intrinsic bias on results either by the sample size or due to a lack of understanding of all the risk factors associated with curve safety. This study aims to narrow this knowledge gap in three aspects: it focuses on urban areas; it uses a large novel GIS dataset of about 25,000 urban curves; and it expands the traditional curve risk factor pool by examining the spatial relationship of curves to adjacent curves and intersections. Using this curve dataset and six years of statewide fatal and injury crash data in the state of Florida, the study develops customized safety performance functions (SPFs) for urban curves based on different spatial relationships of curves to intersections. The results confirm that the traditional risk factors for rural curves, such as traffic volume, curve radius and length, speed limit, functional classification, and the number of lanes, also apply to curves in urban areas. However, the new finding is that curve safety in urban areas is affected by the proximity of curves to adjacent curves and intersections. The curves with intersections and isolated curves (with no adjacent nearby curves) are at high risk. There are also risk factor differences between single and dual-centerline roads. We also observed differences between the travel directions on divided roadway curves, but these differences will require more research.

Clinical predictors of driving simulator performance in drivers with multiple sclerosis.

BACKGROUND: Drivers with multiple sclerosis (MS) may experience visual-cognitive impairment that affects their fitness to drive. Due to limitations associated with the on-road assessment, an alternative assessment that measures driving performance is warranted. Whether clinical indicators of on-road outcomes can also predict driving performance outcomes on a driving simulator are not fully understood. OBJECTIVE: This study examined if deficits in immediate verbal/auditory recall (California Verbal Learning Test-Second Edition; CVLT2-IR) and/or slower divided attention (Useful Field of View™; UFOV2) predicted deficits in operational, tactical, or strategic maneuvers assessed on a driving simulator, in drivers with and without MS. METHODS: Participants completed the CVLT2-IR, UFOV2, and a driving simulator assessment of operational, tactical, and strategic maneuvers. RESULTS: Deficits in immediate verbal/auditory recall and slower divided attention predicted adjustment to stimuli errors, pertaining to tactical maneuvers only, in 36 drivers with MS (vs 20 drivers without MS; F(3, 51) = 6.1, p = 0.001, R2 = 0.3, Radj2=0.2). CONCLUSION: The CVLT2-IR and UFOV2 capture the visual and verbal/auditory recall, processing speed, and divided attention required to appropriately adjust to stimuli in a simulated driving environment. Clinicians may use the CVLT2-IR and UFOV2 as precursors to driving performance deficits in drivers with MS.

Modeling occupant-level injury severity: An application to large-truck crashes.

Most of the injury-severity analyses to date have focused primarily on modeling the most-severe injury of any crash, although a substantial fraction of crashes involve multiple vehicles and multiple persons. In this study, we present an extensive exploratory analysis that highlights that the highest injury severity is not necessarily the comprehensive indicator of the overall severity of any crash. Subsequently, we present a panel, hetroskedastic ordered-probit model to simultaneously analyze the injury severities of all persons involved in a crash. The models are estimated in the context of large-truck crashes. The results indicate strong effects of person-, driver-, vehicle-, and crash-characteristics on the injury severities of persons involved in large-truck crashes. For example, several driver behavior characteristics (such as use of illegal drugs, DUI, and inattention) were found to be statistically significant predictors of injury severity. The availability of airbags and the use of seat-belts are also found to be associated with less-severe injuries to car-drivers and car-passengers in the event of crashes with large trucks. Car drivers' familiarity with the vehicle and the roadway are also important for both the car drivers and passengers. Finally, the models also indicate the strong presence of intra-vehicle correlations (effect of common vehicle-specific unobserved factors) among the injury propensities of all persons within a vehicle.

A comprehensive analysis of factors influencing the injury severity of large-truck crashes.

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data - these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.

In defence of the 'low-mileage bias'.

There has been a long-recognised association between extent of driving and crash involvement: the lower the annual mileage driven, the higher the per-distance crash rate. Because older drivers generally drive less distance per year than others, this association has been used to explain much of their apparent over-involvement in crashes. Several studies from different countries around the world have demonstrated this 'low-mileage bias' and the relative safety of older drivers. However all studies have relied upon self-reported crash involvement and driving activity. Staplin et al. [Staplin, L., Gish, K., Joyce, J., 2008. 'Low mileage bias' and related policy implications-a cautionary note. Accident Analysis and Prevention 40, 1249-1252] have drawn attention to the discrepancy between self-reported and odometer-based driving distances and have argued against the credibility of the low-mileage bias. This paper has re-worked initial data from an early study which supported low-mileage bias, this time using odometer-based readings rather than self-reported mileage. Accepting the odometer readings at face value, the low-mileage bias remains evident, albeit at a reduced level.