Are forensic scientists too risk averse?

Fingerprint examiners maintain decision thresholds that represent the amount of evidence required for an identification or exclusion conclusion. As measured by error rate studies (Proc Natl Acad Sci USA. 2011;108(19):7733-8), these decision thresholds currently exhibit a preference for preventing erroneous identification errors at the expense of preventing erroneous exclusion errors. The goal of this study is to measure the decision thresholds for both fingerprint examiners and members of the general public, to determine whether examiners are more risk averse than potential jury members. To externally measure these decision thresholds, subjects manipulated decision criteria in a web-based visualization that reflects the trade-offs between erroneous identification decisions and erroneous exclusion decisions. Data from fingerprint examiners and the general public were compared to determine whether both groups have similar values as expressed by the placement of the decision criteria. The results of this study show that fingerprint examiners are more risk averse than members of the general public, although they align with error rate studies of fingerprint examiners. Demographic data demonstrate those factors that may contribute to differences in decision criterion placement, both between the two groups and between individuals within a group. The experimental methods provide a rich framework for measuring, interpreting, and responding to the values of society as applied to forensic decision-making.

The effects of drug and alcohol consumption on driver injury severities in single-vehicle crashes.

OBJECTIVE: It is well known that alcohol and drugs influence driving behavior by affecting the central nervous system, awareness, vision, and perception/reaction times, but the resulting effect on driver injuries in car crashes is not fully understood. The purpose of this study was to identify factors affecting the injury severities of unimpaired, alcohol-impaired, and drug-impaired drivers. METHOD: The current article applies a random parameters logit model to study the differences in injury severities among unimpaired, alcohol-impaired, and drug-impaired drivers. Using data from single-vehicle crashes in Cook County, Illinois, over a 9-year period from January 1, 2004, to December 31, 2012, separate models for unimpaired, alcohol-impaired, and drug-impaired drivers were estimated. A wide range of variables potentially affecting driver injury severity was considered, including roadway and environmental conditions, driver attributes, time and location of the crash, and crash-specific factors. RESULTS: The estimation results show significant differences in the determinants of driver injury severities across groups of unimpaired, alcohol-impaired, and drug-impaired drivers. The findings also show that unimpaired drivers are understandably more responsive to variations in lighting, adverse weather, and road conditions, but these drivers also tend to have much more heterogeneity in their behavioral responses to these conditions, relative to impaired drivers. In addition, age and gender were found to be important determinants of injury severity, but the effects varied significantly across all drivers, particularly among alcohol-impaired drivers. CONCLUSIONS: The model estimation results show that statistically significant differences exist in driver injury severities among the unimpaired, alcohol-impaired, and drug-impaired driver groups considered. Specifically, we find that unimpaired drivers tend to have more heterogeneity in their injury outcomes in the presence potentially adverse weather and road surface conditions. This makes sense because one would expect unimpaired drivers to apply their full knowledge/judgment range to deal with these conditions, and the variability of this range across the driver population (with different driving experiences, etc.) should be great. In contrast, we find, for the most part, that alcohol-impaired and drug-impaired drivers have far less heterogeneity in the factors that affect injury severity, suggesting an equalizing effect resulting from the decision-impairing substance.

An empirical analysis of risk-taking in car driving and other aspects of life.

The link between risk-taking behavior in various aspects of life has long been an area of debate among economists and psychologists. Using an extensive data set from Denmark, this study provides an empirical investigation of the link between risky driving and risk taking in other aspects of life, including risk-taking behavior in financial and labor-market decisions. Specifically, we establish significant positive correlations between individuals' risk-taking behavior in car driving and their risk-taking behavior in financial and labor-market decisions. However, we find that the strength of these correlations vary significantly between genders, and across risk decisions. These correlations and their differences across genders get stronger when we construct more "homogenous" groups by restricting our sample to those individuals with at least some stock-market participation. Overall, the empirical results in this study suggest that risk-taking behavior in various aspects of life can be associated, and our results corroborate previous evidence on the link between individuals' risk preferences across various aspects of life. This implies that individuals' driving behavior, which is commonly unobservable, can be more fully understood using observable labor market and financial decisions of individuals.

Safety impacts of signal-warning flashers and speed control at high-speed signalized intersections.

For many years, to reduce the crash frequency and severity at high-speed signalized intersections, warning flashers have been used to alert drivers of potential traffic-signal changes. Recently, more aggressive countermeasures at such intersections include a speed-limit reduction in addition to warning flashers. While such speed-control strategies have the potential to further improve the crash-mitigation effectiveness of warning flashers, a rigorous statistical analysis of crash data from such intersections has not been undertaken to date. This paper uses 10-year crash data from 28 intersections in Nebraska (all with intersection approaches having signal-warning flashers; some with no speed-limit reduction, and the others with either 5 mi/h or 10 mi/h reduction in speed limit) to estimate a random parameters negative binomial model of crash frequency and a nested logit model of crash-injury severity. The estimation findings show that, while a wide variety of factors significantly influence the frequency and severity of crashes, the effect of the 5 mi/h speed-limit reduction is ambiguous-decreasing the frequency of crashes on some intersection approaches and increasing it on others, and decreasing some crash-injury severities and increasing others. In contrast, the 10 mi/h reduction in speed limit unambiguously decreased both the frequency and injury-severity of crashes. It is speculated that, in the presence of potentially heterogeneous driver responses to decreased speed limits, the smaller distances covered during reaction time at lower speeds (allowing a higher likelihood of crash avoidance) and the reduced energy of crashes associated with lower speed limits are not necessarily sufficient to unambiguously decrease the frequency and severity of crashes when the speed-limit reduction is just 5 mi/h. However, they are sufficient to unambiguously decrease the frequency and severity of crashes when the speed-limit reduction is 10 mi/h. Based on this research, speed-limit reductions in conjunction with signal-warning flashers appear to be an effective safety countermeasure, but only clearly so if the speed-limit reduction is at least 10 mi/h.

A multivariate tobit analysis of highway accident-injury-severity rates.

Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments.

A study of factors affecting highway accident rates using the random-parameters tobit model.

A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of highway accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.

The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives.

Reducing the severity of injuries resulting from motor-vehicle crashes has long been a primary emphasis of highway agencies and motor-vehicle manufacturers. While progress can be simply measured by the reduction in injury levels over time, insights into the effectiveness of injury-reduction technologies, policies, and regulations require a more detailed empirical assessment of the complex interactions that vehicle, roadway, and human factors have on resulting crash-injury severities. Over the years, researchers have used a wide range of methodological tools to assess the impact of such factors on disaggregate-level injury-severity data, and recent methodological advances have enabled the development of sophisticated models capable of more precisely determining the influence of these factors. This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.

The effects of road-surface conditions, age, and gender on driver-injury severities.

Drivers' adaptation to weather-induced changes in roadway-surface conditions is a complex process that can potentially be influenced by many factors including age and gender. Using a mixed logit analysis, this research assesses the effects that age, gender, and other factors have on crash severities by considering single-vehicle crashes that occurred on dry, wet, and snow/ice-covered roadway surfaces. With an extensive database of single-vehicle crashes from Indiana in 2007 and 2008, estimation results showed that there were substantial differences across age/gender groups under different roadway-surface conditions. For example, for all females and older males, the likelihood of severe injuries increased when crashes occurred on wet or snow/ice surfaces-but for male drivers under 45 years of age, the probability of severe injuries decreased on wet and snow/ice surfaces - relative to dry-surface crashes. This and many other significant differences among age and gender groups suggest that drivers perceive and react to pavement-surface conditions in very different ways, and this has important safety implications. Furthermore, the empirical findings of this study highlight the value of considering subsets of data to unravel the complex relationships within crash-injury severity analysis.

An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data.

Traditional crash-severity modeling uses detailed data gathered after a crash has occurred (number of vehicles involved, age of occupants, weather conditions at the time of the crash, types of vehicles involved, crash type, occupant restraint use, airbag deployment, etc.) to predict the level of occupant injury. However, for prediction purposes, the use of such detailed data makes assessing the impact of alternate safety countermeasures exceedingly difficult due to the large number of variables that need to be known. Using 5-year data from interstate highways in Indiana, this study explores fixed and random parameter statistical models using detailed crash-specific data and data that include the injury outcome of the crash but not other detailed crash-specific data (only more general data are used such as roadway geometrics, pavement condition and general weather and traffic characteristics). The analysis shows that, while models that do not use detailed crash-specific data do not perform as well as those that do, random parameter models using less detailed data still can provide a reasonable level of accuracy.

A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model.

Pedestrian-injury severity has been traditionally modeled with approaches that have assumed that the effect of each variable is fixed across injury observations. This assumption ignores possible unobserved heterogeneity which is likely to be particularly important in pedestrian injuries because unobserved physical health, strength, and behavior may significantly affect the pedestrians' ability to absorb collision forces. To address such unobserved heterogeneity, this research applies a mixed logit model to analyze pedestrian-injury severity in pedestrian-vehicle crashes. Using police-reported collision data from 1997 through 2000 from North Carolina, several factors were found to more than double the average probability of fatal injury for pedestrians in motor-vehicle crashes including: darkness without streetlights (400% increase in fatality probability), vehicle is a truck (370% increase), freeway (330% increase), speeding involved (360% increase), and collisions involving a motorist who had been drinking (250% increase). It was also found that the effect of pedestrian age was normally distributed across observations, and that as pedestrians became older the probability of fatal injury increased substantially. Heterogeneity in the mean of the random parameters for the freeway and pedestrian-solely-at-fault collision indicators was related to pedestrian gender, and heterogeneity in the mean of the random parameters for the traffic-sign and motorist-back-up indicators was related to pedestrian age.

Zero-state Markov switching count-data models: an empirical assessment.

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents.

Compliance to standardized highway design criteria is considered essential to ensure roadway safety. However, for a variety of reasons, situations arise where exceptions to standard-design criteria are requested and accepted after review. This research explores the impact that such design exceptions have on the frequency and severity of highway accidents in Indiana. Data on accidents at carefully selected roadway sites with and without design exceptions are used to estimate appropriate statistical models of the frequency and severity of accidents at these sites using recent statistical advances with mixing distributions. The results of the modeling process show that presence of approved design exceptions has not had a statistically significant effect on the average frequency or severity of accidents - suggesting that current procedures for granting design exceptions have been sufficiently rigorous to avoid adverse safety impacts. However, the findings do suggest that the process that determines the frequency of accidents does vary between roadway sites with design exceptions and those without.

Markov switching multinomial logit model: An application to accident-injury severities.

In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

Markov switching negative binomial models: an application to vehicle accident frequencies.

In this paper, two-state Markov switching models are proposed to study accident frequencies. These models assume that there are two unobserved states of roadway safety, and that roadway entities (roadway segments) can switch between these states over time. The states are distinct, in the sense that in the different states accident frequencies are generated by separate counting processes (by separate Poisson or negative binomial processes). To demonstrate the applicability of the approach presented herein, two-state Markov switching negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) negative binomial model. It is found that the more frequent state is safer and it is correlated with better weather conditions. The less frequent state is found to be less safe and to be correlated with adverse weather conditions.

A note on modeling vehicle accident frequencies with random-parameters count models.

In recent years there have been numerous studies that have sought to understand the factors that determine the frequency of accidents on roadway segments over some period of time, using count data models and their variants (negative binomial and zero-inflated models). This study seeks to explore the use of random-parameters count models as another methodological alternative in analyzing accident frequencies. The empirical results show that random-parameters count models have the potential to provide a fuller understanding of the factors determining accident frequencies.

Mixed logit analysis of safety-belt use in single- and multi-occupant vehicles.

In this paper we demonstrate a modeling approach that can be used to better understand the use of safety belts in single- and multi-occupant vehicles, and the effect that vehicle, roadway and occupant characteristics have on usage rates. Using data from a roadside observational survey of safety-belt use in Indiana, a mixed (random parameters) logit model is estimated. Potentially interrelated choices of safety-belt use by drivers and front-seat passengers are examined. The approach we use also allows for the possibility that estimated model parameters can vary randomly across vehicle occupants to account for unobserved effects potentially relating to roadway characteristics, vehicle attributes, and driver behavior. Estimation findings indicate that the choices of safety-belt use involve a complex interaction of factors and that the effect of these factors can vary significantly across the population. Our results show that the mixed logit model can provide a much fuller understanding of the interaction of the numerous variables which correlate with safety-belt use than traditional discrete-outcome modeling approaches.

Tobit analysis of vehicle accident rates on interstate highways.

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.

Highway accident severities and the mixed logit model: an exploratory empirical analysis.

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.

Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents.

This research explores differences in injury severity between male and female drivers in single and two-vehicle accidents involving passenger cars, pickups, sport-utility vehicles (SUVs), and minivans. Separate multivariate multinomial logit models of injury severity are estimated for male and female drivers. The models predict the probability of four injury severity outcomes: no injury (property damage only), possible injury, evident injury, and fatal/disabling injury. The models are conditioned on driver gender and the number and type of vehicles involved in the accident. The conditional structure avoids bias caused by men and women's different reporting rates, choices of vehicle type, and their different rates of participation as drivers, which would affect a joint model of all crashes. We found variables that have opposite effects for the genders, such as striking a barrier or a guardrail, and crashing while starting a vehicle. The results suggest there are important behavioral and physiological differences between male and female drivers that must be explored further and addressed in vehicle and roadway design.