The United States Census Bureau reports that rural areas cover about 97% of the nation’s land area and are home to about 60 million people. About 19% of the American population lives in the rural area according to the Census Bureau. Although only 19% of the population lives in rural areas more than 70% of the 4 million miles of roadways in the United States are in rural areas. According to the NHTSA (2021) the fatality rate was 1.5 times higher in rural areas than in urban areas of the US. In Florida, the fatality rate per 100 million VMT(Vehicles Miles Travelled) in rural areas and urban areas were 2.06 and 1.64, respectively, giving a rural to urban fatality rate ratio of about 1.3. This research focuses on analyzing pass-by crashes in rural areas, particularly inFDOT District 3 (Northwest Florida). The primary goal is to identify trends and factors contributing to these crashes and propose interventions aimed at improving transportation safety for rural populations. By focusing on rural transportation, the study aligns with the broader objective of promoting safety in regions that often lack access to infrastructure and transportation resources. The project will explore innovative machine learning and statistical modeling methods to analyze the complex interactions between drivers’ social characteristics and roadway features that influence the frequency and severity of rural pass-by crashes. The findings will inform the development of countermeasures to mitigate risks posed by transportation systems, particularly for populations who live or commute in rural areas.Data needed to train the models were sourced from the Florida Traffic Safety Dashboard, FDOT GIS Open Data Hub and USCensus, focusing on crash events, roadway characteristics and driver demographics. To classify pass-by crashes, distances between crash locations and the drivers’ home ZIP codes were calculated, with a threshold of 30 miles used to define a pass-by crash. Logistic regression and Random Forest models were used to analyze the factors influencing these crashes, with variables such as functional class, weather conditions, vision obstruction, and type of shoulder playing significant roles in predicting crash likelihood. Preliminary results indicate that certain factors, like severe crosswinds, paved shoulders, and specific road classifications, increase the probability of pass-by crashes.Additional future work will focus on refining the models, incorporating additional demographic and roadway data, and further validating the findings.
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