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Kansas Department of Transportation (KDOT) is collaborating with a Kansas State University (KSU) researcher to create a customised crash prediction model for the US state’s rural multi-lane highways.

Research for the model is being carried out by Syeda Rubaiyat Aziz, a KSU doctoral candidate in civil engineering.

Aziz began by calibrating the methodology of the American Association of State Highway and Transport Officials’ (AASHTO) Highway Safety Manual.

As a part of the research, every road segment and intersection of the state is being examined using Google Maps and KDOT video logs.

The process will collect data on the roads, including the number of accidents, driveway density, presence of street lights, and roadside hazard ratings.

KDOT is providing the geometric data and funding required for the project.

Aziz has also developed a Kansas-specific crash prediction tool, which is expected to predict road crashes in the state more accurately than the existing High Safety Manual.

The researcher said: "Saving even a single life would be important for Kansas as well as for the US."

"It is imperative that the model be updated on schedule so that the future crash predictions would be as accurate as possible."

According to the researcher, the results will identify the most dangerous rural road segments and intersections of Kansas.

This will assist in implementing countermeasures and prioritising requests, within the state’s transport budget.

Syeda said: "It is imperative that the model be updated on schedule so that the future crash predictions would be as accurate as possible.

"When you get out on the roads and the conditions are unsafe, you can’t have peace of mind. My research will give the people of Kansas a better quality of life."

Syeda presented her research in February at the 13th Capitol Graduate Research Summit in Topeka, Kansas.

The project is expected to be completed in May.

Image: KSU doctoral candidate in civil engineering Syeda Rubaiyat Aziz. Photo: courtesy of KSU.