There is an increased interest in the use of epidemiological methods in highway safety analysis. The case-control and cohort methods are commonly used in the epidemiological field to identify risk factors and quantify the risk or odds of disease given certain characteristics and factors related to an individual. This same concept can be applied to highway safety where the entity of interest is a roadway segment or intersection (rather than a person) and the risk factors of interest are the operational and geometric characteristics of a given roadway. One criticism of the use of these methods in highway safety is that they have not accounted for the difference between sites with single and multiple crashes. In the medical field, a disease either occurs or it does not; multiple occurrences are generally not an issue. In the highway safety field, it is necessary to evaluate the safety of a given site while accounting for multiple crashes. Otherwise, the analysis may underestimate the safety effects of a given factor. This paper explores the use of the case-control method in highway safety and two variations to account for sites with multiple crashes. Specifically, the paper presents two alternative methods for defining cases in a case-control study and compares the results in a case study. The first alternative defines a separate case for each crash in a given study period, thereby increasing the weight of the associated roadway characteristics in the analysis. The second alternative defines entire crash categories as cases (sites with one crash, sites with two crashes, etc.) and analyzes each group separately in comparison to sites with no crashes. The results are also compared to a "typical" case-control application, where the cases are simply defined as any entity that experiences at least one crash and controls are those entities without a crash in a given period. In a "typical" case-control design, the attributes associated with single-crash segments are weighted the same as the attributes of segments with multiple crashes. The results support the hypothesis that the "typical" case-control design may underestimate the safety effects of a given factor compared to methods that account for sites with multiple crashes. Compared to the first alternative case definition (where multiple crash segments represent multiple cases) the results from the "typical" case-control design are less pronounced (i.e., closer to unity). The second alternative (where case definitions are constructed for various crash categories and analyzed separately) provides further evidence that sites with single and multiple crashes should not be grouped together in a case-control analysis. This paper indicates a clear need to differentiate sites with single and multiple crashes in a case-control analysis. While the results suggest that sites with multiple crashes can be accounted for using a case-control design, further research is needed to determine the optimal method for addressing this issue. This paper provides a starting point for that research.
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http://dx.doi.org/10.1016/j.aap.2012.05.013 | DOI Listing |
PLoS One
January 2025
Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei City, Taiwan.
Background And Objective: Relevant research has provided valuable insights into risk factors for bicycle crashes at intersections. However, few studies have focused explicitly on three common types of bicycle crashes on road segments: overtaking, rear-end, and door crashes. This study aims to identify risk factors for overtaking, rear-end, and door crashes that occur on road segments.
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December 2024
UCF Smart & Safe Transportation Lab, Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, United States. Electronic address:
Intersections are frequently identified as crash hotspots for roadways in major cities, leading to significant human casualties. We propose crash likelihood prediction as an effective strategy to proactively prevent intersection crashes. So far, no reliable models have been developed for intersections that effectively account for the variation in crash types and the cyclical nature of Signal Phasing and Timing (SPaT) and traffic flow.
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December 2024
Global Data Insights & Analytics, Ford Motor Company, United States. Electronic address:
Police crash reports have traditionally been the primary data source for research and development projects aimed at improving traffic safety. However, there are important limitations of such data, particularly the relative infrequency of crashes on a site-by-site basis in many contexts. Crash analyses often require multiple years of data and the use of such data for short-term evaluations creates challenges.
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December 2024
Queensland University of Technology, School of Civil and Environmental Engineering, Brisbane, Australia. Electronic address:
Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts.
View Article and Find Full Text PDFG3 (Bethesda)
December 2024
School of BioSciences, The University of Melbourne, Parkville, VIC 3010, Australia.
Gene drives have enormous potential for solving biological issues by forcing the spread of desired alleles through populations. However, to safeguard from the potentially irreversible consequences on natural populations, gene drives with intermediate outcomes that neither fixate nor get removed from the population are of outstanding interest. To elucidate the conditions leading to intermediate gene drive outcomes, a stochastic, individual allele-focused gene drive model was developed to simulate the diffusion of a homing gene drive in a population.
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