Mei Chen

Crashes on a roadway are influenced by various factors, including but not limited to road geometries, traffic volume, and environmental conditions. Among these factors, traffic volume and segment length are commonly used to predict crashes. Recently, the role of speed in crashes has been recognized as a significant factor, prompting its incorporation as a variable in crash modeling. Nevertheless, previous research studies that examined speed-related factors are mostly concentrated on higher functional class roads where speed data are abundant. Lack of actual speed data has limited the scope of such a study on rural two-lane highways. Due to recent advancements in data collection methodologies, there has been a significant increase in the accessibility of speed data pertaining to these roads. This study aims to assess the significance of speed as a predictor of crashes on rural two-lane highways, utilizing actual speed data. The results of this study showed a negative correlation between speed and crash frequency on rural two-lane roadways. In addition, it was observed that the impact of speed in the crash model becomes more pronounced at higher operating speed conditions of these roads. The aforementioned observation prompted us to consider a categorizer based on speed and, afterwards, separating crash prediction models for various speed ranges. This approach ultimately resulted in enhanced accuracy in crash prediction. Based on our analysis, developing separate models at different speed levels is recommended to better evaluate the safety performance of these roads under various conditions. Such models can also be useful for transportation planners and policymakers to identify high-risk segments and allocate resources to improve the safety of these roads.