Relative Crash Risk Research Articles

Assessing bicycle crash risks controlling for detailed exposure: A Copenhagen case study

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure.The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017–2020 in the greater Copenhagen area (N = 4877).The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7–8) and afternoon peak hours (15–18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol.In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

Assessing a Connected Environment’s Safety Impact During Mandatory Lane-Changing: A Block Maxima Approach

Previous studies have mostly hypothesised or provided preliminary evidence that crash risk during mandatory lane-changing manoeuvres in a connected environment would be reduced. However, the unavailability of crash data for a connected environment makes it challenging to confirm such a hypothesis. To this end, this study has adopted an Extreme Value Theory approach to estimate and compare crash risk using traffic conflicts during mandatory lane-changing manoeuvres in the traditional and connected environments. Using the CARRS-Q advanced driving simulator, seventy-eight participants performed mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication (PC), and connected environment with communication delay (CD). Driving-related factors obtained from the driving simulator data such as speeds, spacings, lag gaps, and remaining distances were used as input to a block maximum (or generalised extreme value, GEV) model. The characteristics of the estimated GEV distributions and relative crash risk were employed as an indicator of safety. Results reveal that in the connected environment driving conditions, the mandatory lane-changing crash risk is significantly reduced compared to the baseline, with the highest reduction observed in the PC condition. The crash risk is found to be higher in the CD condition compared with the PC condition. Findings of this study confirm (i) that the connected environment has the potential to reduce the crash risk substantially by assisting drivers during mandatory lane-changing manoeuvres, and (ii) the efficacy of GEV models to quantify crash risk by using conflict data when crash records are unavailable.

Understanding the Role of Rainfall Intensity on Relative Car Crash Risk in the Carolinas

Abstract As a significant detriment to physical and mental health, millions of motor vehicle crashes occur in the United States each year, with approximately 23% of these crashes linked to adverse weather conditions. This study builds upon a strong knowledge base to provide a deeper understanding of how rainfall intensity influences relative crash risk. Gridded precipitation and temperature data were aggregated to the county level and analyzed alongside motor vehicle crash data for all 146 counties in the Carolinas (North Carolina and South Carolina) for the period 2003–19. A matched-pair analysis routine linked unique time steps of rainfall (daily, 6-h, and hourly) to corresponding dry periods to evaluate relative crash risk across each state. Risk estimates were calculated on the basis of precipitation thresholds (light, moderate, heavy, and very heavy). Results indicate a statistically significant increase in crash risk during periods of rainfall in the Carolinas. As a baseline, the relative risk of experiencing a crash increases by 11.6% during days with accumulating rainfall and as much as 81.0% during heavy rainfall events over a 6-h period. In general, estimates of risk increase relative to the intensity of the rainfall event and the temporal delineation of the matched-pair routine. However, these relationships have unique spatiotemporal patterns indicating that, although hourly risk estimates may be beneficial for urban counties, daily relative risk estimates may be the only way to accurately capture risk in rural areas. Significance Statement Each year, more than 1 000 000 motor vehicle crashes in the United States are linked to adverse weather conditions in police reports, with rainfall events being among the largest contributors to increased crash risk. In this study, crash frequencies are evaluated to better understand how the intensity of rainfall events (light vs heavy) influences the risk of experiencing a collision on roadways in North Carolina and South Carolina. The results of statistical analyses revealed that risk increases significantly during rainfall events in both states and that the risk of experiencing a crash is highest during the heaviest rainfall events. However, even during light precipitation events, the risk of experiencing a crash is significantly higher than when driving during dry conditions. These results are helpful to transportation stakeholders and emergency responders in the hope of reducing crash risk in our changing climate.

Behavioural adaptation or constraint? The effect of curve proximity on driving behaviour

Horizontal curves are typically associated with increased crash risk when compared with straight roads, but recent analyses have suggested that having more frequent sharp curves decreases the relative crash risk posed by each curve. Here, 90 drivers completed a simulated rural drive with either high proximity (160 m straight tangent between curves) or low proximity (1200 m tangent) curves. Curve proximity had a significant effect on approach speeds, with drivers in the high proximity curve drive showing significantly lower mean and maximum approach speeds before entering the curve. However, they also showed an unexpected tendency to higher speeds while negotiating the curve itself. The current study provides direct empirical evidence that driving behaviour on approach to a given curve is significantly affected by the proximity of other curves, and therefore highlights the need to factor in the characteristics of the road on approach to the curve, as well as the features of the curve itself when assessing risk.

Lowering the legal alcohol limit in Belgium: Potential effects on the number of traffic victims

BackgroundSince 1994, the legal limit of Blood Alcohol Concentration (BAC) is 0.5 g/L for the general drivers’ population in Belgium. Since 2015, this limit has been lowered to 0.2 g/L for professional drivers. So far, no specific limitation has been adopted for novice drivers in Belgium. Recently, two bills were submitted to the House of Representatives: the first one proposed to impose a zero-limit for every driver, the second one proposed to restrict this zero-limit only to novice drivers. ObjectiveThe present study evaluated the potential impact of the two bills that aim to reduce the legal BAC limit from 0.5 to zero, either for all drivers or for novice drivers only. We elaborated three scenarios related to the BAC categories for which lowering the legal BAC limit to zero would affect drink-driving behaviour:“Targeted BAC levels” scenario assumed that the new policy would impact only the specifically targeted BAC category, i.e., drivers in the category BAC below 0.5 g/L.“Extended impact” scenario corresponded to the “Targeted BAC levels” scenario to which we added a “halo effect” on drivers in the BAC category “0.5 g/L ≤ BAC < 0.8 g/L”.“Broad range impact” scenario corresponded to the “Extended impact” scenario to where we extended the “halo effect” to drivers in the BAC category “0.8 g/L ≤ BAC < 1.2 g/L”. MethodsThe effect estimates were based on firstly, scientific literature on risks related to drink-driving at different BAC-levels, secondly data on crashes in Belgium and thirdly data on drink-driving behaviour in Belgium and in other European countries. ResultsIn case of a zero limit for all drivers, an annual reduction can be expected of 10 to 17 fatalities (i.e., a decrease of 2.4% to 3.9%), 8 to 20 severe injuries (i.e., a decrease of 0.3% to 0.8%) and 135 to 315 slight injuries (i.e., a decrease of 0.4% to 0.8%). In case a zero limit is only applied to novice drivers, an annual decrease can be expected by 2 to 4 fatalities (i.e., a decline of 3.7% to 6.2%), 8 to 16 serious injuries and 135 to 262 slight injuries (i.e., a decline of 1.7% to 3.2% in both cases). DiscussionThere is no clear evidence on which of the three scenarios would be the most plausible. As the relative risk of a car crash increases strongly with the BAC level, the success of either measure will strongly depend on its ability to also affect drink driving at concentrations that are forbidden already. This also means that most of the casualties could be prevented if compliance with current rules increased.

An Extreme Value Theory approach to estimate crash risk during mandatory lane-changing in a connected environment

Examining crash risk in the highly anticipated connected environment is hindered by its novelty and the consequent scarcity of relevant data. This study proposes an Extreme Value Theory approach to examine and quantify mandatory lane-changing crash risk in the traditional and connected environments using traffic conflict techniques. The CARRS-Q advanced driving simulator was utilised to collect trajectory data of 78 participants performing mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication, and connected environment with communication delay. Using the exceedance statistics theory (also known as a Peak Over Threshold approach corresponding to Generalised Pareto distribution), three separate models corresponding to each driving condition were developed. Driving-related factors obtained from the driving simulator data, such as speeds, spacings, lag gaps, and remaining distances, as well as driver demographics, were used as input variables to these models. Relative crash risk analysis and characteristics of the fitted Generalised Pareto distributions were employed as indicators of safety. The findings suggest that the connected environment significantly reduces mandatory lane-changing crash risk compared with the baseline condition, with the highest risk reduction observed in the perfect communication condition. While the crash risk of the communication delay condition is higher than that of the perfect communication condition, it is lower than the baseline condition. Furthermore, a comparison of the developed model to its counterpart (i.e., Block Maxima approach) showed the better performance of the adopted approach. The findings of this study provide insights into the positive impact of the connected environment on the safety of mandatory lane-changing manoeuvres as well as confirm the veracity of Peak Over Threshold models in estimating crash risk using traffic conflict data.

Evaluation of a warning system to reduce the risk of casualty crashes at rural junctions in South Australia

Objective This study evaluated the effectiveness and potential safety benefits of a Rural Junction Active Warning System (RJAWS), which uses variable speed limit signs (VSLSs) to temporarily impose a reduced speed limit along the major road when another vehicle is approaching on the minor road or turning into the minor road from the far-side major leg. Methods The RJAWS was trialed at 4 3-leg rural junctions between 2-lane roads in South Australia. A quasi-experimental before–after analysis was conducted. The free-flow speed of vehicles along the junction major approaches was used as an indicator of the risk of a casualty crash with adjacent traffic. Events with activated and nonactivated VSLSs after the RJAWS installation were analyzed separately. Travel speed, speed limit compliance, and the relative risk of a casualty crash were compared. The potential risk of casualty crashes relative to traveling at the default speed limit was calculated based on a known relationship between travel speed and relative casualty risk for rural roads in South Australia. Results When the reduced speed limit signs are activated, the average travel speed along the major road is reduced between 11.3 and 22.1 km/h, with a consequent reduction of the expected average casualty risk between 42% and 65% compared to before the RJAWS installation. . Though compliance with the reduced posted speed limit was low, the RJAWS still proved effective in reducing travel speed because the majority of drivers traveled through the monitored junctions at speeds below the default speed limit when the sign was activated. Conclusions This evaluation indicated that the trialed RJAWS can potentially reduce the risk of fatal and serious injuries at junctions under conditions that may present the opportunity for an adjacent-direction collision with another vehicle. Extending the RJAWS installation to additional junctions is strongly suggested. Further monitoring is needed to determine whether safety benefits are sustained over the long term.

Validating the representativeness assumption of the quasi-induced exposure method using a national representative field observation survey

Objective The quasi-induced exposure (QIE) method was developed to estimate relative crash risk exposure. A fundamental assumption often made in applying the QIE method is that not-at-fault drivers in clean two-vehicle crashes (i.e., one and only one driver is at-fault) represent the general exposure of the driving population to crash risk in the absence of the intervention being studied. Our study used direct field observation data to test the representativeness of the assumption for not-at-fault drivers obtained from the General Estimating System (GES) crash data, a national crash database in the United States. Methods Distributions of driver gender, age group, vehicle type, and time-of-crash among the not-at-fault drivers in clean two-vehicle crashes (D2) and the ones in two-or-more-vehicle crashes (i.e., all not-at-fault drivers) from the GES data were compared to the driving population estimated from the National Occupant Protection Use Survey (NOPUS), a national representative field observation survey. Results The gender and vehicle-type distributions of D2 and all not-at-fault drivers were not statistically significantly different from the ones in the NOPUS data. Age-group distributions for both not-at-fault driving populations were marginally similar to the ones estimated from NOPUS. Conclusion By system-wide comparisons on gender, age group, vehicle type, and period, our study suggests that the not-at-fault drivers in crash databases with crashes ranging from no injury to fatal injury reflect the general driving population when the collision occurred. Future study should evaluate the representativeness assumption among other important factors, including roadway type, road geometry, and level of urbanization. Our study supports the credibility of applying the QIE method in traffic safety research using crash databases of all crashes with all severities.

Investigation of the Relationship between Rainfall and Fatal Crashes in Texas, 1994–2018

Understanding how crash factors are impacted by rain is critical to road safety planning and management. This study assesses the impact of rain on traffic safety by conducting an analysis of the fatal crashes related to rain in Texas from 1994 to 2018. The fatal crash data was gathered from the Fatality Analysis Reporting System (FARS) database maintained by the National Highway Traffic Safety Administration (NHTSA). Environmental variables used in the analysis include month of the year, time of the day, temperature, and weather condition. The roadway-related factors identified include the posted speed limit, the number of lanes, route sign, and Vehicle Miles Traveled (VMT). The driver-related factors identified include age, gender, and the number of licensed drivers in total. Relative risk analysis was performed to statistically quantify the impact of rainy conditions at the hourly and monthly time scales. On average, rain-related fatal crashes represented about 6.8% of the total fatal crashes. However, the proportion shows higher variability at the annual, monthly, and hourly time scales and seems to be influenced by other factors such as the age and gender of the driver, type of the road, and posted roadway speed limit. Total and rain-related crashes show statistically significant decreasing trends when normalized by the total number of licensed drivers or vehicle miles travelled. The relative risk of a fatal crash during rainy conditions was always greater than 1.0 at hourly, monthly, and annual time scales. However, it shows significant variability at the monthly (1.07 to 2.78) and hourly scales (1.35 to 2.57). The relative risk is higher in less urbanized and drier counties, in general. Gender and age analysis reveals that male and young drivers are more likely to be involved in a fatal crash but less likely to be killed in the crash.

Does space influence on the frequency and severity of the distraction-affected vehicle crashes? An empirical evidence from the Central Ohio

This study investigates spatial dependencies between frequency and within severity of vehicle crashes caused by distracted driving, along with the role of the built and socio-demographic environments in the Columbus Metropolitan Area, Ohio. We adopt a full Bayesian hierarchical framework with Multivariate Conditional Autoregressive Priors to account for the complex spatial correlation structure as well as the unobserved heterogeneity. Using aggregated crash count data (Property Damage Only and Bodily Injuries) for the 414 census tracts, the analysis outcomes reveal that census tracts providing more jobs and having a higher proportion of commercial land use would have higher likelihood of relative crash risks in both severity levels. Inclusion of correlation structure between frequency as well as within crash-severity-level has proven a significant increase on the performance of the model, verifying influences of space on the frequency and severity of distraction-affected vehicle crashes. In addition, this research presents areas of higher relative risks (spatial clusters) that have 1.5 times elevated risk of collision than other census tracts. The identification of areas of excessive risks informs us to devise policies to mitigate negative consequences of distraction-affected crashes.

Life-threatening alcohol-related traffic crashes in adverse weather: a double-matched case–control analysis from Canada

ImportanceDrunk driving is a major cause of death in North America, yet physicians rarely counsel patients on the risks of drinking and driving.ObjectiveTo test whether the risks of a life-threatening...

Methods for investigating crash risk: Comparing case–control with responsibility analysis

Objective: There are 2 primary methods for establishing relative risk: case–control studies, in which crash and matched control data are collected separately, and responsibility analysis, which exploits a single existing crash database by using nonresponsible drivers as an “induced exposure” control group (which is less expensive and therefore more feasible for examining the large number of substances that can impact driving behavior). Though both approaches are scientifically sound and methodologically valid, each approach has its own inherent obstacles to overcome. In this article, we examine in detail how different criteria for the development of control cases influence the accuracy of crash risk estimates for drivers with positive blood alcohol concentrations (BACs).Methods: We applied responsibility analysis to crash-involved drivers in a recent crash case–control study, thereby providing 2 sets of control cases: Those from responsibility analysis and those from the case control study.Results: Case–control and responsibility analysis crash risk curves did not differ significantly, indicating that both systems generate valid estimates of the relative crash risk of drivers on the road.Conclusions: The results suggest that when researchers are faced with finance or time constraints that make case–control studies infeasible, responsibility analysis should be considered a viable alternate methodological approach.

Effects of methodological decisions on rainfall-related crash relative risk estimates

Numerous studies have examined the influence of rainfall on the relative risk of crash, and they all agree that rainfall leads to an increase in relative risk as compared to dry conditions; what they do not agree on is the magnitude of these increases. Here we consider three methodological decisions made in computing the relative risk and examine their impacts: the inclusion or exclusion of zero total events (where no crashes occur during event or control periods), the temporal scale of analysis, and the use of information on pavement and weather conditions contained with the crash reports to determine relative risk. Our analyses are based on several years of data from six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota and Ohio). Zero total events in the context of weather related crash studies typically provide no information on the actual crash odds and greatly alter the distribution of relative risk estimates and should be removed from the analysis. While the use of a daily time step provides an estimate of relative risk that is not significantly different from an hourly time step for the majority of rural counties in our study area, the same is true of only 39% of the urban counties. Finally, the use of pavement and weather condition information from the crash reports results in relative risk estimates that are lower than the standard approach, however this difference decreases as rainfall totals increase. By highlighting the influence of methodological choices, we hope to pave the way towards the potential reduction in uncertainties in weather-related relative risk estimates.

Risk and safety perception on urban and rural roads: Effects of environmental features, driver age and risk sensitivity

ABSTRACTObjective: The ability to detect changing visual information is a vital component of safe driving. In addition to detecting changing visual information, drivers must also interpret its relevance to safety. Environmental changes considered to have high safety relevance will likely demand greater attention and more timely responses than those considered to have lower safety relevance. The aim of this study was to explore factors that are likely to influence perceptions of risk and safety regarding changing visual information in the driving environment. Factors explored were the environment in which the change occurs (i.e., urban vs. rural), the type of object that changes, and the driver's age, experience, and risk sensitivity.Methods: Sixty-three licensed drivers aged 18–70 years completed a hazard rating task, which required them to rate the perceived hazardousness of changing specific elements within urban and rural driving environments. Three attributes of potential hazards were systematically manipulated: the environment (urban, rural); the type of object changed (road sign, car, motorcycle, pedestrian, traffic light, animal, tree); and its inherent safety risk (low risk, high risk). Inherent safety risk was manipulated by either varying the object's placement, on/near or away from the road, or altering an infrastructure element that would require a change to driver behavior. Participants also completed two driving-related risk perception tasks, rating their relative crash risk and perceived risk of aberrant driving behaviors.Results: Driver age was not significantly associated with hazard ratings, but individual differences in perceived risk of aberrant driving behaviors predicted hazard ratings, suggesting that general driving-related risk sensitivity plays a strong role in safety perception. In both urban and rural scenes, there were significant associations between hazard ratings and inherent safety risk, with low-risk changes perceived as consistently less hazardous than high-risk impact changes; however, the effect was larger for urban environments. There were also effects of object type, with certain objects rated as consistently more safety relevant. In urban scenes, changes involving pedestrians were rated significantly more hazardous than all other objects, and in rural scenes, changes involving animals were rated as significantly more hazardous. Notably, hazard ratings were found to be higher in urban compared with rural driving environments, even when changes were matched between environments.Conclusion: This study demonstrates that drivers perceive rural roads as less risky than urban roads, even when similar scenarios occur in both environments. Age did not affect hazard ratings. Instead, the findings suggest that the assessment of risk posed by hazards is influenced more by individual differences in risk sensitivity. This highlights the need for driver education to account for appraisal of hazards’ risk and relevance, in addition to hazard detection, when considering factors that promote road safety.

The close to home effect in road crashes

The notion that most crashes happen close to home has been repeated so often it has come to be an accepted truth. Despite this, to our knowledge there have not been any studies to date which have adequately accounted for rates of exposure in drawing conclusions concerning relative crash risk and distance from home. We addressed this gap by using data representative of all travel (from the New Zealand Household Travel Survey) and crashes in New Zealand, by New Zealand drivers, from 1 July 2013 to 30 June 2014. Trip origins, destinations and driver home address were used to convert 31,102 trips into travel exposure on roads at increasing distance from home. Travel data were compared with crash distance from home for 6,295 injury crashes involving 9,315 drivers. Analysis showed that on average, drivers were indeed more likely to crash close to home. Roads within 11km (6.8miles) of home accounted for half of all travel and 62% of all crashes. The ‘close to home’ effect held for male and female drivers. Novice (learner) drivers were the only demographic subgroup to not exhibit the close to home effect. Compared with crashes further away, crashes close to home were more likely to involve alcohol and diverted attention, and less likely to involve driver fatigue. These findings provide a mandate for continued investigation into the science of typical, everyday driving. We contend that behavioural effects associated with driving on familiar roads may be a factor in injury crash risk for experienced drivers.

Evaluating Crash Risk in Urban Areas Based on Vehicle and Pedestrian Modeling

This article presents a method for investigating the spatial distribution of vehicle and pedestrian traffic crashes relative to the volume of vehicle and pedestrian movement in urban areas. This method consists of two phases. First, vehicle and pedestrian traffic volumes on the street network are modeled using a space syntax configurational analysis of the network, land use data, and observed traffic data. Second, crash prediction models are fitted to the traffic crash data, using negative binomial regression models and based on traffic volume estimates and street segment lengths. The method was applied in two areas in Tel Aviv, Israel, which differ in their morphological and traffic characteristics. The case‐studies illustrated the method's capability in identifying hazardous locations on the network and examining relative crash risks. The analysis shows that an increase in vehicle or pedestrian traffic volume tends to be associated with a decrease in relative crash risk. Moreover, the spatial patterns of relative crash risks are associated with the design characteristics of urban space: areas characterized by dense street networks encourage more walking, and are generally safer for pedestrians, while those with longer street segments encourage more driving, are less safe for pedestrians, but safer for vehicles.

Accounting for Driver Distraction and Socioeconomic Characteristics in a Crash Risk Index: Naturalistic Driving Study

Distracted driving has long been acknowledged as one of the main contributors to crashes in the United States. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, few studies attempted to quantify that influence. This study proposed a crash risk index (CRI) to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis was conducted with data from the SHRP 2 Naturalistic Driving Study. The proposed CRI was developed on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g., age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g., age older than 60) on the likelihood of engagement in secondary tasks. Logistic regression analysis was performed on the secondary tasks, socioeconomic attributes, and specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks in each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considers the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

Effects of Rainfall on Vehicle Crashes in Six U.S. States

Abstract Rainfall is one of many types of weather hazard that can lead to motor vehicle crashes. To better understand the link between rainfall and crash rates, daily gridded precipitation data and automobile crash data are gathered for six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota, Ohio) for the period 1996–2010. A matched pair analysis is used to pair rainfall days with dry days to determine the relative risk of crash, injury, and fatality. Overall, there is a statistically significant increase in crash and injury rates during rainfall days of 10% and 8%, respectively, leading to an additional 28 000 crashes and 12 000 injuries in the 1 May–30 September period each year relative to what would be expected if those days were dry. The risk of crashes and injuries increases for increasing daily rainfall totals, with an overall increase in crashes and injuries of 51% and 38% during days with more than 50 mm (2 in.) of rainfall. While urban counties and rural counties with and without interstates each saw increased crash risk during rainfall, urban counties saw the most significant increases in relative risk. There are a number of exceptions to these broad spatial patterns, indicating that relative risk varies in ways that are not explained solely by meteorological factors.

Evaluation of the Total Eyes-off-Road Time Glance Criterion in the NHTSA Visual-Manual Guidelines

NHTSA issued a 12-s total eyes-off-road time (TEORT) glance criterion for its visual-manual guidelines simulator test. This criterion relied on NHTSA’s analysis of manual radio-tuning data from its simulator study using a 2010 Toyota Prius premium radio and its test-track study of five 2005 to 2010 vehicles using various radio-tuning control types. However, this 12-s criterion was biased falsely downward. First, NHTSA oversampled the youngest participants (with the shortest TEORT scores) in the simulator study and oversampled the newest radios (with the shortest TEORT scores) in the test track study. Second, NHTSA incorrectly assumed that track and simulator metrics had a one-to-one correspondence, but the track TEORT was substantially shorter than the simulator TEORT for a matched Prius task with matched older participant ages. Third, NHTSA set its criterion at exactly the 85th percentile of the TEORT data rather than its upper confidence limit; hence, traditional radio-tuning tasks would fail repeated tests simply because of random test-to-test variation. The current study, after adjusting for these and other biases, estimated a criterion of 21.4 s from the identical NHTSA data sets. Emerging knowledge that relies on naturalistic driving studies suggests that cumulative glance metrics such as TEORT have low predictive validity for crashes compared with other metrics. A new test should be developed with metrics of higher predictive validity for relative crash risk on the basis of a growing scientific consensus about the relationship—both positive and negative—between eye glances, relative crash risk, and secondary task performance while driving.

On the Effects of Listening and Talking to Humans and Devices on Driving.

The body of research on cognitive distraction while driving is vast and spans many decades. To this research, the authors of the target article add three experiments that measure a number of cognitive tasks across laboratory, simulation, and on-road contexts. The pattern of decrements is similar across contexts, when expressed as an index, and when compared to previous research. Measurement, task, and generalizability issues arise from the approaches taken by the authors. For example, the use of "pure" cognitive tasks may not necessarily generalize to everyday driving behavior, wherein visual and physical distractions are inherently interleaved with cognitive tasks. A valuable contribution of the authors' future research on cognitive distractions would be to predict relative crash risk.