Safety 2025, 11(1), 1; https://doi.org/10.3390/safety11010001 - 30 Dec 2024
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Horse-and-buggy transportation, vital for many rural communities and the Amish population, has been largely overlooked in safety research. This study examines the characteristics and injury severity of horse-and-buggy roadway crashes in Michigan’s rural areas. Detailed crash data are essential for safety studies, as
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Horse-and-buggy transportation, vital for many rural communities and the Amish population, has been largely overlooked in safety research. This study examines the characteristics and injury severity of horse-and-buggy roadway crashes in Michigan’s rural areas. Detailed crash data are essential for safety studies, as crash scene descriptions are mainly found in narratives and diagrams. However, extracting and utilizing this information from traffic reports is challenging. This research tackles these challenges using image-processing and text-mining techniques to analyze crash diagrams and narratives. The study employs the AlexNet convolutional neural network (CNN) to identify and extract horse-and-buggy crashes, analyzing (2020–2023) Michigan UD-10 rural crash reports. Natural Language Processing (NLP) techniques also identified primary risk factors from crash narratives, analyzing single-word patterns (“unigrams”) and sequences of three consecutive words (“trigrams”). The findings emphasize the risks involved in horse-and-buggy interactions on rural roadways and highlight various contributing factors to the severity of these crashes, including distracted or careless actions by motorists, nighttime visibility issues, and failure to yield, especially by elderly drivers. This study suggests prioritizing horse-and-buggy riders in road safety and public health programs and recommends comprehensive measures that could significantly reduce crash incidence and severity, improving overall safety in Michigan’s rural areas, including better signage, driver education, and community outreach. Also, the study highlights the potential of advanced image-processing techniques in traffic safety research that could lead to more precise and actionable findings, enhancing road safety for all users.
Full article
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<p>Data extraction framework.</p> Full article ">Figure 2
<p>Crash narratives analysis flow.</p> Full article ">Figure 3
<p>Unigram displayed as word clouds. (<b>a</b>) for minor/no injury; (<b>b</b>) for fatal/serious injury.</p> Full article ">Figure 4
<p>(<b>a</b>) Trigram word frequency for fatal/serious injury outcome for horse-and-buggy crashes. (<b>b</b>) Trigram word frequency for minor/no injury outcome for horse-and-buggy crashes.</p> Full article ">Figure 5
<p>The confusion matrix of the logistic regression model.</p> Full article ">
<p>Data extraction framework.</p> Full article ">Figure 2
<p>Crash narratives analysis flow.</p> Full article ">Figure 3
<p>Unigram displayed as word clouds. (<b>a</b>) for minor/no injury; (<b>b</b>) for fatal/serious injury.</p> Full article ">Figure 4
<p>(<b>a</b>) Trigram word frequency for fatal/serious injury outcome for horse-and-buggy crashes. (<b>b</b>) Trigram word frequency for minor/no injury outcome for horse-and-buggy crashes.</p> Full article ">Figure 5
<p>The confusion matrix of the logistic regression model.</p> Full article ">
