Traffic fatalities in the US have declined significantly in recent years–from a modern high of 43,510 in 2005 to 32,719 in 2013. Similar declines have occurred in Iowa, where fatalities have declined from 450 to 317 over this same period. These declines are attributable to various factors, including enhanced vehicle safety features, targeted safety-related legislation and enforcement programs, and the introduction of various engineering countermeasures. Intersections and horizontal curves are two high-priority emphasis areas where engineering countermeasures are often applied. Intersections account for 30 percent of crashes in rural areas and 6 percent of all fatal crashes. Motor vehicle crash injury rates are higher in rural areas, due in part to increased emergency medical services (EMS) response times, reliance on volunteer EMS, and increased transport times. Horizontal curves also present heightened crash rates, as the Fatality Analysis Reporting System (FARS) indicates more than 25 percent of fatal crashes in the US occur on horizontal curves. The percentage of fatal curve-related crashes is higher on rural roads due to the predominance of horizontal curves, especially on two-lane roadways in rural areas. The average crash rate for horizontal curves is about three times greater than on highway tangents. Consequently, intersections and horizontal curves present two high-priority areas for engineering countermeasures. The implementation of countermeasure programs are generally focused on high-risk locations, which are identified based on extensive historical traffic safety data (i.e., crash history). For example, the Moving Ahead for Progress in the 21st Century (MAP-21) Act requires all states to have in place a Highway Safety Improvement Program (HSIP) that “emphasizes a data-driven, strategic approach to improving highway safety on all public roads that focuses on performance.” Unfortunately, the identification of candidate locations for engineering countermeasures is often challenging due to the random and rare nature of traffic crashes, as well as related analytical issues such as regression-to-the-mean (RTM). These challenges are particularly pronounced on rural highways, where many potentially high-risk locations may be difficult to identify given lower traffic volumes. Given the prevailing focus on safety decisions that are data-driven, much research has focused on gaining a more thorough understanding of how various risk factors affect the frequency of traffic crashes, injuries, and fatalities at specific roadway sites. The extant research literature has shown various factors to affect the frequency of traffic crashes, including traffic volume, roadway geometry, type of traffic control, and other factors. Such risk factors are likely to vary across different types of road facilities, such as highway segments, intersections, and interchanges. Gaining a better understanding of the complex relationships between crash risk and roadway geometry provides important information to aid in the development of targeted policies and programs to reduce traffic crashes and the resultant injuries and fatalities. This study aims to provide assistance in the identification of risk factors for traffic crashes on two facility types: intersections and horizontal curves. These risk factors are identified through the analysis of a robust database, which combines data from various sources including traffic volumes, roadway geometry, and other characteristics. Ultimately, the results of this research will allow for more effective network surveillance and identification of high-risk locations. (Author/publisher)
Samenvatting