Assessing the collective safety of automated vehicle groups: A duration modeling approach of accumulated distances between crashes.

Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of automated vehicles more quickly, this paper uses the collective crash histories of a group of automated vehicles, and applies a duration modeling approach to the accumulated distances between crashes.

Intended cycling frequency and the role of happiness and environmental friendliness after COVID-19.

Although the COVID-19 pandemic has contributed to an increase in cycling in many countries worldwide, it is not yet known whether this increase becomes a long-lasting change in mobility. The current study explores this increase by analyzing data collected in a U.S.

A statistical assessment of work-from-home participation during different stages of the COVID-19 pandemic.

Responses to the COVID-19 pandemic have dramatically transformed industry, healthcare, mobility, and education. Many workers have been forced to shift to work-from-home, adjust their commute patterns, and/or adopt new behaviors. Particularly important in the context of mitigating transportation-related emissions is the shift to work-from-home.

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 role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions.

The effect of inappropriate speed adjustment to adverse conditions on crash-injury severities, and how this effect might vary across male and female drivers, and over time, is not well understood. To study this, single-vehicle crashes occurring in rainy weather, where speed too fast for conditions is a driver action identified as a contributing factor to the crash, were considered. The differences between the resulting crash-injury severities of male and female drivers (and how these differences change over time) is then studied utilizing three years of Florida crash data and estimating random parameters multinomial logit models of driver injury severity while considering potential heterogeneity in the means and variances of parameter estimates.

Unobserved heterogeneity and the effects of driver nationality on crash injury severities in Saudi Arabia.

This paper investigates factors that significantly contribute to the injury severity of different drivers of different nationality backgrounds. Using the data from Riyadh, Saudi Arabia, a random parameters multinomial logit model of driver-injury severity was estimated to explore the effects of a wide range of variables on driver injury-severity outcomes. With three possible outcomes (no injury, injury, fatality), only single-vehicle crashes are considered and crashes involving domestic (Saudi) and international (non-Saudi) drivers were modeled separately.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

Probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes.

Motorcycle fatalities have more than doubled in the United States since 1997--highlighting the need to better understand the many interrelated factors that determine motorcyclists' crash-injury severities. In this paper, using a detailed crash database from the state of Indiana, we estimate probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes. Nested logit (estimated with full information maximum likelihood) and standard multinomial logit model results show a wide-range of factors significantly influence injury-severity probabilities.

Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence.

Introduction: This study explores the differences in injury severity between male and female drivers, and across the different age groups, in single-vehicle accidents involving passenger cars.

Method: Given the occurrence of an accident, separate male and female multinomial logit models of injury severity (with possible outcomes of no injury, injury, and fatality) were estimated for young (ages 16 to 24), middle-aged (ages 25 to 64), and older (ages 65 and older) drivers.

Results: The estimation results show statistically significant differences in the factors that determine injury-severity levels between male and female drivers and among the different driver age groups.