2021 May 2021

Crunching numbers to prevent car crashes

SMU statistical scientists take big data on the road with a predictive model showing potential to influence positive driving behaviors and identify accident hotspots.

A predictability model built by an SMU research team can calculate the odds that certain variables – such as drunk driving or speeding 20 miles above the limit – will result in a severe car accident.

Tony Ng, an SMU statistical science professor and one of the co-creators of the model, says the tool could be especially useful as an educational tool for making concrete the real impact of certain driving conditions and behaviors to different audiences, like young drivers.
“This can hopefully influence drivers’ behavior positively and reduce crashes by making drivers more aware of dangerous driving habits,” Ng says.
Using data analytic techniques such as the SMU model to analyze traffic data also can potentially identify accident hotspots and the reasons behind them, helping traffic officials improve road safety. For example, the data could be used to adjust speed limits on a given highway or prompt the placement of cameras where drivers are known to speed, Ng says.
Read more at SMU Research.

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