Karl Kim

Personal and behavioral predictors of automobile crash and injury severity

The purpose of this paper is to develop a statistical model explaining the relationships between certain driver characteristics and behaviors, crash severity, and injury severity. Applying techniques of categorical data analysis to comprehensive data on crashes in Hawaii during 1990, we build a structural model relating driver characteristics and behaviors to type of crash and injury severity. The structural model helps to clarify the role of driver characteristics and behaviors in the causal sequence leading to more severe injuries. From the model we estimate the effects of various factors in terms of odds multipliers--that is, how much does each factor increase or decrease the odds of more severe crash types and injuries. We found that driver behaviors of alcohol or drug use and lack of seat belt use greatly increase the odds of more severe crashes and injuries. Driver errors are found to have a small effect, while personal characteristics of age and sex are generally insignificant. We conclude with a discussion of our modeling approach and of the implications of our findings for appropriate traffic safety interventions and future research.

Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators

The spatial relationship between activities which generate trips and motor vehicle accidents is examined and applied to the City and County of Honolulu for 1990. A method is described for examining spatial variations in motor vehicle accidents, aggregated into small geographical areas. A spatial lag model is developed which examines the zonal relationship of motor vehicle accidents to population, employment and road characteristics. The resulting model is tested for each hour of the day, weekdays and weekends separately. The predictors of accidents fluctuate according to different trip generating activities and change considerably over the day. The method can generate expected accidents, allowing for an identification of areas which have higher than expected levels. It is argued that this method focuses attention on characteristics of neighborhoods and areas, and not just on the road system.

Influence of Land Use, Population, Employment, and Economic Activity on Accidents

In this study, the relationships between land use, population, employment by sector, economic output, and motor vehicle accidents are explored. Through the use of comprehensive data from the largest county in Hawaii, the relationships are modeled in a uniform 0.1-mi2 (0.259-km2) grid structure and with various linear regression models. This method has an advantage over other approaches that have typically used unevenly sized and shaped traffic analysis zones, census tracts, or block groups. Positive, statistically significant relationships among population, job counts, economic output, and accidents are identified. After some of the general effects are sorted through, a negative binomial (NB) model is used to look at the absolute and relative effects of these factors on the number of pedestrian, bicycle, vehicle-to-vehicle, and total accidents. With a multivariate model, the different effects can be compared and the specific nature of the relationships between zonal characteristics and accidents can be id...

DAILY FLUCTUATIONS IN HONOLULU MOTOR VEHICLE ACCIDENTS

Changes in daily motor vehicle accidents during 1990 are examined for the City and County of Honolulu. Adjusting for changed reporting criteria, daily accidents fluctuate according to an interaction between traffic volume, weekday travel patterns, holidays and weather. Fridays, particularly, and Saturdays have more daily accidents. Minor holidays generate more daily accidents, but major holidays generate fewer daily accidents, primarily because of lower traffic volume. Rainfall increases the risk of accidents substantially. The interaction between afternoons and rainfall is particularly dangerous. Unemployment also appears to reduce daily accidents.

PREDICTORS OF SAFETY BELT USE AMONG CRASH-INVOLVED DRIVERS AND FRONT SEAT PASSENGERS: ADJUSTING FOR OVER-REPORTING

Police-reported crash data are rarely used to investigate safety belt use and its predictors, even though these data have a number of advantages over data collected in roadside surveys. It has been widely recognized that motorists tend to over-report their safety belt use to police when mandatory belt use becomes law. In this paper, we use a logistic regression model that allows for misclassification errors in outcome variable to examine predictors of safety belt use among crash-involved drivers and front seat passengers. Our analysis shows significant associations between occupant characteristics, driving circumstances, and safety belt use. Alcohol involvement has the strongest negative association with safety belt use, but this association would be considerably underestimated without adjusting for the over-reporting of safety belt use in police-reported crash data. The adjusted belt use rate among front seat occupants with at least nonincapacitating injuries is about 81%, compared to 90% in police-reported crash data.

Drivers at Fault: Influences of Age, Sex, and Vehicle Type

Using log-linear modeling techniques, the probability of fault among collision-involved drivers in Hawaii is related to three categorical variables: age, sex, and vehicle type. Very young and very old drivers face up to three times the risk of being at fault compared to middle-aged drivers. Substantial gender effects also occur at both ends of the age distribution. Pickup truck drivers have higher odds of being at fault than automobile drivers, while van drivers have lower odds. An ‘‘odds multiplier’’ computation combines the effects of the variables to permit comparison of the relative odds of fault between various categories of drivers. The implications of these findings are reviewed in terms of policy responses toward these high-risk combinations.

MOTOR VEHICLE CRASHES AND LAND USE: EMPIRICAL ANALYSIS FROM HAWAII

At first glance, the relationship between crashes and land use appears obvious. Various types of land uses tend to generate and attract different types of trips, and trip-making behavior affects the nature and volume of traffic. As the use of land intensifies, it does not seem unreasonable to expect that the potential exposure to crashes would also increase. Yet upon closer inspection, it is evident that crashes are more a function of the characteristics of drivers and travelers than the underlying uses of land. Using comprehensive police crash data linked to a land use database, the relationships between land use and automobile crashes in Hawaii are investigated. Recent developments in geographic information system technology and the availability of spatial databases provide a rich source of information with which to investigate the relationships between crashes and the environments in which they occur. Although the patterns in terms of crashes and the underlying use of land are difficult to describe, the implications of these findings for planners, developers, and those interested in traffic safety are apparent.

Personal, temporal and spatial characteristics of seriously injured crash-involved seat belt non-users in Hawaii

The characteristics of crash-involved seat belt non-users in a high use state (Hawaii) are examined in order to better design enforcement and education programs. Using police crash report data over a 10-year period (1986-1995), we compare belted and unbelted drivers and front seat occupants, who were seriously injured in crashes, in terms of personal (age, gender, alcohol involvement, etc.) and crash characteristics (time, location, roadway factors, etc.). A logistic regression model combined with the spline method is used to analyze and categorize the salient differences between users and non-users. We find that unbelted occupants are more likely to be male, younger, unlicensed, intoxicated and driving pickup trucks versus other vehicles. Moreover, non-users are more likely than users to be involved in speed-related crashes in rural areas during the nighttime. Passengers are 70 times more likely to be unbelted if the driver is also unbelted than passengers of vehicles with belted drivers. While our general findings are similar to other seat belt studies, the contribution of this paper is in terms of a deeper understanding of the relative importance of various factors associated with non-use among seriously injured occupants as well as demonstrating a powerful methodology for analyzing safety problems entailing the categorization of various groups. While the former has implication for seat belt enforcement and education programs, the latter is relevant to a host of other research questions.

Modeling Fault Among Bicyclists and Drivers Involved in Collisions in Hawaii, 1986-1991

Subsequent to a review of trends in collisions between bicyclists and motorists in Hawaii during the period 1986 to 1.991, characteristics of bicyclists and drivers involved in crashes are compared. On the basis of police-reported crash data it can be concluded that bicyclists tend to be young, male, and, not surprisingly, more likely to be seriously injured than motorists in bicycle-motor-vehicle collisions. Bicyclists are much less likely to be attributed with inattention than motorists, and slightly less likely to be attributed with misjudgment or alcohol or drug use than motorists. Bicyclists, however, are much more likely than motorists to disregard traffic controls or go the wrong way on a street just before becoming involved in a collision. Motorists are more likely than bicyclists to fail to yield, to engage in improper overtaking, or to follow too closely before becoming involved in a collision. The largest proportion of bicycle collisions occurs during the period 3:30 to 6:30 p.m. Other temporal...

Finding Fault in Motorcycle Crashes in Hawaii: Environmental, Temporal, Spatial, and Human Factors

Patterns of fault among drivers and motorcycle riders involved in collisions in Hawaii are examined. Personal and behavioral characteristics of drivers and riders involved in crashes are described, then temporal, roadway, and environmental factors associated with crashes between motorcycles and other motor vehicles are discussed. An argument is made that focusing on fault provides a strategic starting point for educational and traffic enforcement programs for drivers and motorcycle riders alike. A fault model is built by using logistic regression to predict the odds of fault for motorcyclists and vehicles involved in crashes. The spatial distribution of at-fault motorcyclists and drivers is mapped to determine if there are distinct spatial patterns for enforcement and educational efforts. The implications for motorcycle safety, driver education, law enforcement, and traffic safety research are discussed.