A Kansas State University researcher is partnering with the Kansas Department of Transportation (KDOT) to develop a state-specific crash prediction model for rural multilane highways.
“Saving even a single life would be important for Kansas as well as for the U.S.,” said Syeda Rubaiyat Aziz, who is a doctoral candidate in civil engineering.
Aziz’s research began with calibrating the American Association of State Highway and Transport Officials’ (AASHTO) Highway Safety Manual methodology. Utilizing Google Maps and KDOT video logs to examine each roadway segment and intersection for obtaining number of crashes and a variety of other factors, including the presence of lighting posts, driveway density and roadside hazard rating, she also analyzed roadway geometric data provided by KDOT, which is funding the research project.
A Kansas-specific crash prediction tool, her research indicates that this model performs better than the existing High Safety Manual in predicting crashes in Kansas. Aziz categorizes crashes into three severity levels: fatal crashes, injury crashes and property damage-only crashes. She further divides injury crashes into three subcategories: incapacitating, non-incapacitating and possible injuries.
Aziz foresees a two-step use for her research results. First, her findings are useful in identifying the most hazardous or unsafe segments and intersections of rural roadways. Then, the conclusions can direct in finding suitable countermeasures and thereby prioritizing requests within the state’s transportation budget.
The KDOT-funded project is expected to be completed in May. Upon completion, KDOT will use Aziz’s research findings and readjust its model to current data for future projects.
