South Carolina traffic fatality report for 2013

This statistical sheet shows 2009-2013 traffic fatality comparisons through January, 2013, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina traffic fatality report for 2013

This statistical sheet shows 2009-2013 traffic fatality comparisons through May, 2013, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina traffic fatality report for 2013

This statistical sheet shows 2009-2013 traffic fatality comparisons through July, 2013, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina traffic fatality report for 2013

This statistical sheet shows 2009-2013 traffic fatality comparisons through September, 2013, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina traffic fatality report for 2014

This statistical sheet shows 2010-2014 traffic fatality comparisons through January, 2014, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina traffic fatality report for 2016

This statistical sheet shows traffic fatality comparisons through May, 2016, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

South Carolina Traffic Collision Report Form (TR- 310) and Supplement Truck and Bus Report Form Instruction Manual

The instructions in this manual have been prepared to provide guidance for completing the South Carolina Traffic Collision Report Form TR-310 and the Supplemental Bus and Truck Collision Report Form. It lists traffic laws and definitions and gives examples of traffic collision report forms.

South Carolina traffic fatality report for 2016

This statistical sheet shows traffic fatality comparisons through August, 2016, fatalities by route category, top fatality counties, fatalities by restraint usage and top crash events.

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.

The crash involvement of older drivers is associated with their hazard perception latencies

Hazard perception in driving is the one of the few driving-specific skills associated with crash involvement. However, this relationship has only been examined in studies where the majority of individuals were younger than 65. We present the first data revealing an association between hazard perception and self-reported crash involvement in drivers aged 65 and over. In a sample of 271 drivers, we found that individuals whose mean response time to traffic hazards was slower than 6.68 seconds (the ROC-curve derived pass mark for the test) were 2.32 times (95% CI 1.46, 3.22) more likely to have been involved in a self-reported crash within the previous five years than those with faster response times. This likelihood ratio became 2.37 (95% CI 1.49, 3.28) when driving exposure was controlled for. As a comparison, individuals who failed a test of useful field of view were 2.70 (95% CI 1.44, 4.44) times more likely to crash than those who passed. The hazard perception test and the useful field of view measure accounted for separate variance in crash involvement. These findings indicate that hazard perception testing and training could be potentially useful for road safety interventions for this age group.

Older drivers’ insight into their hazard perception ability

Even though the driving ability of older adults may decline with age, there is evidence that some individuals attempt to compensate for these declines using strategies such as restricting their driving exposure. Such compensatory mechanisms rely on drivers’ ability to evaluate their own driving performance. This paper focuses on one key aspect of driver ability that is associated with crash risk and has been found to decline with age: hazard perception. Three hundred and seven drivers, aged 65 to 96, completed a validated video-based hazard perception test. There was no significant relationship between hazard perception test response latencies and drivers’ ratings of their hazard perception test performance, suggesting that their ability to assess their own test performance was poor. Also, age related declines in hazard perception latency were not reflected in drivers’ self-ratings. Nonetheless, ratings of test performance were associated with self-reported regulation of driving, as was self-rated driving ability. These findings are consistent with the proposal that, while self-assessments of driving ability may be used by drivers to determine the degree to which they restrict their driving, the problem is that drivers have little insight into their own driving ability. This may impact on the potential road safety benefits of self-restriction of driving because drivers may not have the information needed to optimally self-restrict. Strategies for addressing this problem are discussed.