A Framework for Identifying Roadway Characteristics Affecting Speeding-Related Crashes in Rural Areas

Speeding is a major concern on all roadways and is a leading factor in traffic fatalities and serious injuries. Rural roadways are often disproportionately impacted by these traffic crashes and fatalities, despite the lower traffic volumes and populations. It is important to address this speeding issue, especially in rural areas, which can be done with an organized plan, such as a Speed Management Action Plan (SMAP), and collaboration with all parties involved. The goal of this research is to provide a framework to help rural areas identify locations that are at higher risk of speeding-related crashes by analyzing roadway characteristics that have a higher likelihood of a speeding-related crash to occur and which characteristics have a larger proportional influence associated with them. Identifying these roadway characteristics can help focus state crash analysis or countermeasure implementation to ensure that locations that are at highest risk of speeding-related crashes are receiving appropriate and effective speed management countermeasures.

The framework identifies roadway characteristics that are more likely to contribute to speeding-related crashes, focusing on rural, non-interstate, and non-intersection roads. It underscores the importance of data-driven decision-making to prioritize high-risk locations and optimize resource allocation. By providing states with tools and information, the framework facilitates the identification of critical factors influencing speeding-related crashes, such as roadway alignment, surface conditions, and lighting. Additionally, it provides comprehensive guidance on data collection, data filtering, key characteristics to identify, data analysis, prioritizing findings, applying the results, and monitoring the implementations. This structured approach not only supports the effective use of crash data for informed decision-making but also aligns with the development and execution of SMAPs.

The research utilized Virginia Department of Motor Vehicles Traffic Records Electronic Data System (TREDS) crash data for the year 2022 to create the framework. The roadway characteristics included in the analysis were determined using engineering judgement and past studies. Those characteristics were identified from the attributes: location of the first harmful event, light conditions, roadway alignment, roadway description, roadway defects, and roadway surface conditions. A proportional analysis method was used to calculate the speeding-related crash likelihood percentage and the systemic impact percentage for each characteristic included in the analysis. Key findings from this analysis revealed certain roadway characteristics, such as roadside, darkness, curved, two-way not divided, and wet surfaces that had high impacts on both the likelihood of a speeding-related crash occurring and high systemic impact. Virginia crash data was used in this study to test the framework developed to show its effectiveness and how it can be utilized by other states.