Truck-involved crashes within the rural network pose a distinctive and multifaceted challenge that necessitates a specialized approach for comprehensive analysis and effective mitigation. Data from the Insurance Institute for Highway Safety (IIHS) indicates that fatal truck crashes frequently occur between 12:00 p.m. and 3:00 p.m., deviating from patterns observed in other vehicle crashes. This peculiarity arises from the distinct routines of truck drivers, often commencing journeys early, which amplifies the risk of driver fatigue and contributes to crashes during the early hours. Recognizing the unique characteristics of rural areas necessitates acknowledging that truck-involved crashes within these regions can have varied effects on vulnerable groups, including low-income households, minorities, outdoor workers, and older adults. These disparities emphasize the crucial need for an all encompassing approach to enhance safety. Leveraging the context of Florida, this project endeavors to formulate a robust methodology that incorporates geospatial, optimization, and machine learning techniques. By applying the specialized Two-Step Floating Catchment Area (2SFCA) method, this proposal aims to bridge the gap between rest area provisions, truck driver behavior, and rural truck-involved crashes. The data-driven insights generated through this project have the potential to significantly elevate rural roadway safety in Florida. This initiative intends to establish a clear correlation between the availability of rest areas, particularly truck parking lots, and the frequency of truck-involved crashes on rural roadways. The objective is to offer data-driven insights that guide strategic rest area development, thereby contributing to the reduction of truck-related accidents and fostering safer rural highways acrossFlorida.Signal4Analytics (S4A) and the Fatality Analysis Reporting System (FARS) serve as optimal platforms for collecting truck-involved crash data. Furthermore, the 2SFCA analysis acknowledges the distinctive features of trucking operations. It encompasses the delineation of catchment areas surrounding each rest area, adapting to rural travel conditions and operational constraints. Calculating accessibility scores for each rural catchment area factors in elements such as the availability of truck parking lots and the population density within each region. These scores provide insights into the potential utilization of rest areas by truck drivers in rural environments. This process uncovers patterns and hotspot locations, linking these incidents with computed accessibility scores to unveil potential associations between rest area accessibility and rates of truck-involved accidents in rural settings.
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