A civil engineer out of Kansas State University has partnered with the Kansas Department of Transportation to generate a crash prediction model for “rural multi-lane highways”.
Syeda Rubaiyat Aziz is working towards her doctorate in civil engineering and says, “Saving even a single life would be important for Kansas, as well as for the United States.”
The research began with looking at the American Association of State Highway and Transport Officials’ Highway Safety Manual methodology. Then using Google Maps – which has comprehensive traffic statistics in real time – and video from highways in Kansas to study roadways and intersections that have experienced crashes. She was funded by the Kansans Department of Transport that specializes in road and bridge maintenance, transportation planning, contract compliance and transportation-related administrative support.
According to GCTelegram, she categorized road accidents into levels of severity: “fatal crashes, injury crashes, and property-damage-only crashes”. Then she cross-references those details with the regular accidents that occur and which intersections they occurred at. She then tries to find a way around how these accidents happen so that something can be put in place to prevent them.
Sunanda Dissanayake, a professor of civil engineering, said, “Rural highway safety is a critical issue in Kansas, and the number of fatalities due to motor-vehicle crash involvement is considerable. Finding ways to save lives will bring enormous benefits on all fronts.”
Elsewhere in the world, in Ghana, engineers have also tried to create a crash prediction tool. What they found in 2011 when they published Crash prediction model for two-lane rural highways in the Ashanti region of Ghana, was that individual crash prediction models would have to be made for every individual highway because no highway in the world is the same.
Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. Fletcher et al found that due to wide differences in traffic mix, road quality, design and road user behaviour, it would be neither valid nor useful to apply simple multiplicative factors or even devise more complex conversion formulae for models developed elsewhere for another country.
Therefore, the work of engineers in their own state, provinces and countries are invaluable in crash prediction tools to perhaps save the lives and ensure a better quality of life for drivers whilst cars are still fully manual. Once cars are automated, it could be that the cars calculate the crash predictions by themselves.
Source: WIBW News Now