Fred L. Mannering

Effect of roadway geometrics and environmental factors on rural freeway accident frequencies.

This paper explores the frequency of occurrence of highway accidents on the basis of a multivariate analysis of roadway geometrics (e.g. horizontal and vertical alignments), weather, and other seasonal effects. Based on accident data collected in the field, a negative binomial model of overall accident frequencies is estimated along with models of the frequency of specific accident types. Interactions between weather and geometric variables are proposed as part of the model specifications. The results of the analysis uncover important determinants of accident frequency. By studying the relationship between weather and geometric elements, this paper offers insight into potential measures to counter the adverse effects of weather on highway sections with challenging geometrics.

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.

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.

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.

A DYNAMIC EMPIRICAL ANALYSIS OF HOUSEHOLD VEHICLE OWNERSHIP AND UTILIZATION

In this article we develop a dynamic model of household vehicle ownership and utilization behavior by using data that were generated before, during, and after the 1979 energy crisis. The principal empirical findings are that households have maintained a distinct preference for American over foreign cars, but also have strong brand loyalties. The results suggest that notwithstanding recent financial trends, U.S. firms must continue to make technological improvements in their vehicles and must combat the brand loyalty that has developed for foreign vehicles if the domestic automobile industry is to be viable at the present scale of operations.

IMPACT OF ROADSIDE FEATURES ON THE FREQUENCY AND SEVERITY OF RUN-OFF-ROADWAY ACCIDENTS: AN EMPIRICAL ANALYSIS

In the US, single-vehicle run-off-roadway accidents result in a million highway crashes with roadside features every year and account for approximately one third of all highway fatalities. Despite the number and severity of run-off-roadway accidents, quantification of the effect of possible countermeasures has been surprisingly limited due to the absence of data (particularly data on roadside features) needed to rigorously analyze factors affecting the frequency and severity of run-off-roadway accidents. This study provides some initial insight into this important problem by combining a number of databases, including a detailed database on roadside features, to analyze run-off-roadway accidents on a 96.6-km section of highway in Washington State. Using zero-inflated count models and nested logit models, statistical models of accident frequency and severity are estimated and the findings isolate a wide range of factors that significantly influence the frequency and severity of run-off-roadway accidents. The marginal effects of these factors are computed to provide an indication on the effectiveness of potential countermeasures. The findings show significant promise in applying new methodological approaches to run-off-roadway accident analysis.

MODELING ACCIDENT FREQUENCIES AS ZERO-ALTERED PROBABILITY PROCESSES : AN EMPIRICAL INQUIRY

This paper presents an empirical inquiry into the applicability of zero-altered counting processes to roadway section accident frequencies. The intent of such a counting process is to distinguish sections of roadway that are truly safe (near zero-accident likelihood) from those that are unsafe but happen to have zero accidents observed during the period of observation (e.g. one year). Traditional applications of Poisson and negative binomial accident frequency models do not account for this distinction and thus can produce biased coefficient estimates because of the preponderance of zero-accident observations. Zero-altered probability processes such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) distributions are examined and proposed for accident frequencies by roadway functional class and geographic location. The findings show that the ZIP structure models are promising and have great flexibility in uncovering processes affecting accident frequencies on roadway sections observed with zero accidents and those with observed accident occurrences. This flexibility allows highway engineers to better isolate design factors that contribute to accident occurrence and also provides additional insight into variables that determine the relative accident likelihoods of safe versus unsafe roadways. The generic nature of the models and the relatively good power of the Vuong specification test used in the non-nested hypotheses of model specifications offers roadway designers the potential to develop a global family of models for accident frequency prediction that can be embedded in a larger safety management system.

The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies

This research provides a demonstration of a statistical model of accident frequency that can eventually be used as part of a proactive program to allocate safety-related highway improvement funds. Negative binomial regressions of annual accident frequency on sections of principal arterials in Washington State were estimated using data from two years (1992 and 1993). In all, 31306 observations were used in model estimation (annual accident frequencies on specific sections of highway). Our estimation results isolated the effects of various highway geometric and traffic characteristics on annual accident frequency. Subsequent elasticity computations identified the relative importance of the variables included in our specifications. The findings show that the negative binomial regression used in this paper is a powerful predictive tool and one that should be increasingly applied in future accident frequency studies.

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 womens 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.

AN EXPLORATORY MULTINOMIAL LOGIT ANALYSIS OF SINGLE-VEHICLE MOTORCYCLE ACCIDENT SEVERITY

Abstract Most previous research on motorcycle accident severity has focused on univariate relationships between severity and an explanatory variable of interest (e.g., helmet use). The potential ambiguity and bias that univariate analyses create in identifying the causality of severity has generated the need for multivariate analyses in which the effects of all factors that influence accident severity are considered. This paper attempts to address this need by presenting a multinomial logit formulation of motorcyclerider accident severity in single-vehicle collisions. Five levels of severity are considered: 1. (a) property damage only, 2. (b) possible injury, 3. (c) evident injury, 4. (d) disabling injury, and 5. (e) fatality. Using 5-year statewide data on single-vehicle motorcycle accidents from the state of Washington, we estimate a multivariate model of motorcycle-rider severity that considers environmental factors, roadway conditions, vehicle characteristics, and rider attributes. Our findings show that the multinomial logit formulation that we use is a promising approach to evaluate the determinants of motorcycle accident severity.