Police-reported Crash Data Research Articles

Using data linkage to generate 30-day crash-fatality adjustment factors for Taiwan

Different countries have their own police reporting time standards for counting the number of fatalities in reported crashes. A rapid estimation method (such as adjustment factor) for the comparison is important. The data-linkage technique was used to combine police-reported crash data and vital registration data, in order to generate 30-day fatality adjustment factors for various reporting time standards, which could also shed light on the fatal injury trend over time. The major findings were as follows. Firstly, a conservative 30-day fatality adjustment factor for the first day (or 24 h) would be 1.54 (or 1.35) in an area with a large motorcycle population, like Taiwan. This produced 20–40% higher 30-day fatalities than UK Transport Research Laboratory predicted, and 15–25% higher fatalities than those in Europe/Japan. Secondly, after excluding motorcycle impacts, the Taiwanese factors suggested 8–14% higher fatalities within 30 days than those in Europe/Japan. Third, motorcycle fatalities influenced the overall 30-day fatality trend within 3 days. In the future, both the police under-reporting problem and the motorcycle/overall fatal injury pattern within 3 days after crashing in developing countries like Taiwan merit further investigation.

Helmet use and motorcycle fatalities in Taiwan

Motorcycle deaths accounted for more than half of total traffic fatalities in Taiwan in 2002. This study uses the police-reported crash data from Taiwan between 1999 and 2001 to estimate the effectiveness of helmets, simultaneously taking into account of sample selection bias. Sample selection arises because helmet usage will affect the probability of death or injury, which in turn influences whether a crash is included in the data. The results show that sample selection does not seriously bias the estimate of helmet effectiveness and helmets reduce the probability of death in a crash by 40%, which is higher than what was previously found. Without helmets, the number of motorcyclists killed in 2001 would have jumped by 51%. The estimated proportion of helmeted motorcyclists has increased from 71 to 78% between 1999 and 2001, suggesting that helmet use is rising after the implementation of mandatory helmet law in 1997. Also, helmets significantly reduce the likelihood of head and neck injuries in a crash by 53%, and lead to a 71% reduction in the probability of death caused by head and neck injuries.

An accident waiting to happen: a spatial approach to proactive pedestrian planning

There are about 75,000 pedestrian crashes in the United States each year. Approximately 5000 of these crashes are fatal, accounting for 12% of all roadway deaths. On college campuses, pedestrian exposure and crash-risk can be quite high. Therefore, we analyzed pedestrian crashes on the campus of the University of North Carolina at Chapel Hill (UNC) as a test case for our spatially-oriented prototype tool that combines perceived-risk (survey) data with police-reported crash data to obtain a more complete picture of pedestrian crash-risk. We use spatial analysis techniques combined with regression models to understand factors associated with risk. The spatial analysis is based on comparing two distributions, i.e. the locations of perceived-risk with police-reported crash locations. The differences between the two distributions are statistically significant, implying that certain locations on campus are perceived as dangerous, though pedestrian crashes have not yet occurred there, and there are actual locations of police-reported crashes that are not perceived to be dangerous by pedestrians or drivers. Furthermore, we estimate negative binomial regression models to combine pedestrian and automobile exposure with roadway characteristics and spatial/land use information. The models show that high exposure, incomplete sidewalks and high crosswalk density are associated with greater observed and perceived pedestrian crash-risk. Additionally, we found that people perceive a lower risk near university libraries, stadiums, and academic buildings, despite the occurrence of crashes.

A simple method for identifying and correcting crash problems on urban arterial streets

Urban arterials by their nature carry heavy traffic volumes and generate large numbers of motor vehicle crashes. The present study involved review of police crash reports to identify precrash events and driver actions for a sample of crashes on urban arterials and describes a method for reducing such crashes based on analyses of collision patterns and identification of locations with excessive numbers of crashes of a particular type. Police-reported crash data were obtained for three urban arterials in the Washington, DC metropolitan area. A total of 2013 crash reports were analyzed. Seven crash types accounted for nearly 90% of these reports. On each arterial studied, several locations with excessive numbers of crashes of a particular type were identified, and corresponding engineering countermeasures were recommended. Differences between the approach employed in this study and traditional blackspot analyses are discussed.

Estimating Driver Crash Risks Based on the Extended Bradley–Terry Model: An Induced Exposure Method

Summary Quantifying driver crash risks has been difficult because the exposure data are often incompatible with crash frequency data. Induced exposure methods provide a promising idea that a relative measurement of driver crash risks can be derived solely from crash frequency data. This paper describes an application of the extended Bradley–Terry model for paired preferences to estimating driver crash risks. We estimate the crash risk for driver groups defined by driver–vehicle characteristics from log-linear models in terms of a set of relative risk scores by using only crash frequency data. Illustrative examples using police-reported crash data from Hawaii are presented.

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.

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...

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 1991, 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 and locational features of bicycle collisions are described. Among the most significant findings presented is that motorists in Hawaii are much more likely to be classified at fault than bicyclists. Whereas motorists are at fault in approximately 83.5 percent of incidents, bicyclists are at fault in only 16.5 percent of incidents. A logistic model is developed and used to explain the likelihood of motorists being at fault in collisions with bicyclists. Covariates that increase the likelihood of motorist fault include motorist age (squared), bicyclist age, bicyclist helmet use, and motorist turning actions. Variables that decrease the likelihood of motorist fault include motorist age, bicyclist age (squared), bicyclist alcohol use, bicyclists making turning actions, and rural locations.

Adoption of right turn on red: Effects on crashes at signalized intersections

By the end of the 1970's, all states in the U.S. had modified their laws to permit drivers to turn right on steady red at signalized intersections. Police-reported crash data from six states where permissive right turn on red (RTOR) laws were adopted during 1974–1977, as well as data from three states where the law in effect was unchanged throughout the period, were used to determine the effect of adopting such laws on the frequency of crashes involving right turning maneuvers at signalized intersections. The increase in the overall frequency of such crashes in states that adopted permissive right turn on red laws exceeded by more than 20% the comparable change in states that retained the same laws. Larger increases were found in urban areas (25%), and for pedestrian crashes (57%) especially in urban areas (79%). An increase of over 30% was found for child pedestrians, 100% for adults, and 110% for elderly pedestrians after adoption of RTOR.