The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

Safety impacts due to the incompatibility of SUVs, minivans, and pickup trucks in two-vehicle collisions

This research sets out to estimate the effects of vehicle incompatibility on the risk of death or major injury to drivers involved in two-vehicle collisions.Based on data for 2,999,395 drivers, logistic regression was used to model the risk of driver death or major injury (defined has being hospitalized). Our analyses show that pickup trucks, minivans and sport utility vehicles (SUVs) are more aggressive than cars for the driver of the other vehicle and more protective for their own drivers. The effect of the pickups is more pronounced in terms of aggressivity. The point estimates are comparable to those in the Toy and Hammitt study [Toy, E.L., Hammitt, J.K., 2003. Safety impacts of SUVs, minivans, and pickup trucks in two-vehicle crashes. Risk Analysis 23, 641–650], but, in contrast to that study, we are now able to establish that a greater number of these effects are statistically significant with a larger sample size.Like vehicle mass and type, other characteristics of drivers and the circumstances of the collision influence the driver’s condition after impact. Male drivers, older drivers, drivers who are not wearing safety belts, collisions occurring in a higher speed zone and head-on collisions significantly increase the risk of death. Except for the driver’s sex, all of these categories are also associated with an increased risk of death or of being hospitalized after being involved in a two-vehicle collision. For this risk, a significant increase is associated with female drivers.     

Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity

This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc.Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists.This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists.     

A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes

Leaving the scene of a crash without reporting it is an offence in most countries and many studies have been devoted to improving ways to identify hit-and-run vehicles and the drivers involved. However, relatively few studies have been conducted on identifying factors that contribute to the decision to run after the crash. This study identifies the factors that are associated with the likelihood of hit-and-run crashes including driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. Using a logistic regression model to delineate hit-and-run crashes from nonhit-and-run crashes, this study found that drivers were more likely to run when crashes occurred at night, on a bridge and flyover, bend, straight road and near shop houses; involved two vehicles, two-wheel vehicles and vehicles from neighboring countries; and when the driver was a male, minority, and aged between 45 and 69. On the other hand, collisions involving right turn and U-turn maneuvers, and occurring on undivided roads were less likely to be hit-and-run crashes.     

Quasi-induced exposure method: Evaluation of not-at-fault assumption

Crash rates are used to establish the relative safety of various variables of concern such as driver classes, vehicle types and roadway components. Appropriate exposure data for estimating crash rates is critical but crash databases do not contain information on driver or vehicle exposure. The quasi-induced exposure method, which uses not-at-fault driver/vehicle data as an exposure metric, is a technique used in order to overcome this problem. The basic assumption made here is that not-at-fault drivers represent the total population in question. This paper examines the validity of this assumption using the Kentucky crash database to define two samples of not-at-fault drivers. One sample included only not-at-fault drivers selected from the first two vehicles in a multi-vehicle crash (two or more vehicles involved) while the other included the not-at-fault drivers from multi-vehicle crashes with more than two vehicles involved and excluding the first two drivers. The assumption is that the randomness of the involvement of drivers in the second sample is more reasonable than the drivers in the first two vehicles involved in crashes. The results indicate that these two samples are similar; there is no statistical evidence demonstrating that both samples represent two different populations in the maneuvers and other variables/factors examined here; and they are representative simple random samples of the driver population with respect to the distribution of the driver age when there is no reasonable doubt about investigating officers’ judgments. Thus, estimating relative crash propensities for any given driver type by using the quasi-induced exposure approach will yield reasonable estimates of exposure.     

What does the driver look at? The influence of intersection characteristics on attention allocation and driving behavior

One of the main contributing factors with intersection accidents is lack of information due to attention allocation. In many cases, drivers fail to yield right of way to other traffic participants. One reason is that drivers have inappropriate expectations about a traffic situation. They allocate their attention primarily to certain areas of the intersection but neglect others. In a driving simulator study, the influence of intersection complexity on drivers’ expectations and their driving behavior was examined. In two T-intersections, the complexity was varied by the traffic density (low and high) using either one or two important objects: vehicles (left) with or without pedestrians (right). Additionally, the reaction to two critical incidents in close proximity of the intersections was examined. Gaze behavior, vehicle reactions, and subjective data were recorded. 40 subjects (26 male, 14 female, M = 31.0 years, SD = 11.9 years) participated in the study. Interestingly, the least complex intersections showed the most accidents which was interpreted as the result of inadequate attention allocation. It was shown that both the drivers’ attention allocation and vehicle velocity when turning off were responsible for this effect. The results contribute to a better understanding of the role of drivers’ expectation and attention allocation in the causation of intersection accidents.     

Quasi-induced exposure: The choice of exposure metrics

The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure.There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others.We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.     

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 65–96, 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, whileself-assessments of driving ability may be used by drivers to determine the degree to which they restrict their driving, the problem is that drivershave 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.     

Critical older driver errors in a national sample of serious U.S. crashes

Objective

Older drivers are at increased risk of crash involvement per mile traveled. The purpose of this study was to examine older driver errors in serious crashes to determine which errors are most prevalent.

Methods

The National Highway Traffic Safety Administration’s National Motor Vehicle Crash Causation Survey collected in-depth, on-scene data for a nationally representative sample of 5470 U.S. police-reported passenger vehicle crashes during 2005–2007 for which emergency medical services were dispatched. There were 620 crashes involving 647 drivers aged 70 and older, representing 250,504 crash-involved older drivers. The proportion of various critical errors made by drivers aged 70 and older were compared with those made by drivers aged 35–54.

Results

Driver error was the critical reason for 97% of crashes involving older drivers. Among older drivers who made critical errors, the most common were inadequate surveillance (33%) and misjudgment of the length of a gap between vehicles or of another vehicle’s speed, illegal maneuvers, medical events, and daydreaming (6% each). Inadequate surveillance (33% vs. 22%) and gap or speed misjudgment errors (6% vs. 3%) were more prevalent among older drivers than middle-aged drivers. Seventy-one percent of older drivers’ inadequate surveillance errors were due to looking and not seeing another vehicle or failing to see a traffic control rather than failing to look, compared with 40% of inadequate surveillance errors among middle-aged drivers. About two-thirds (66%) of older drivers’ inadequate surveillance errors and 77% of their gap or speed misjudgment errors were made when turning left at intersections. When older drivers traveled off the edge of the road or traveled over the lane line, this was most commonly due to non-performance errors such as medical events (51% and 44%, respectively), whereas middle-aged drivers were involved in these crash types for other reasons. Gap or speed misjudgment errors and inadequate surveillance errors were significantly more prevalent among female older drivers than among female middle-aged drivers, but the prevalence of these errors did not differ significantly between older and middle-aged male drivers. These errors comprised 51% of errors among older female drivers but only 31% among older male drivers.

Conclusions

Efforts to reduce older driver crash involvements should focus on diminishing the likelihood of the most common driver errors. Countermeasures that simplify or remove the need to make left turns across traffic such as roundabouts, protected left turn signals, and diverging diamond intersection designs could decrease the frequency of inadequate surveillance and gap or speed misjudgment errors. In the future, vehicle-to-vehicle and vehicle-to-infrastructure communications may also help protect older drivers from these errors.     

Errors in motion processing amongst older drivers may increase accident risk

Accident statistics highlight that older drivers are more frequently involved in right-of-way collisions than younger drivers. Accurately gauging vehicle speed is critical for judgement of when to pull out from a junction safely in front of oncoming traffic. We used psychophysical methods to measure drivers’ ability to discriminate between different rates of looming presented by vehicles approaching at different speeds. We demonstrate that sensitivity to approach speed reduces by between 2.8 and 3.4 mph, dependent upon vehicle type, for every decade that age increases. We show that perceptual limitations for drivers over the age of 75 years can lead to a 50% reduction in time available to perform traffic manoeuvres, which may contribute in part to their overrepresentation in casualty statistics at junction. Results are discussed in terms of implications for road safety policy.