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When Iowa’s snow piles up, TensorFlow can keep roads safe

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Iowa may be heaven, but it’s a snowy one. With an average of around 33 inches of snow every year, keeping roads open and safe is an important challenge. Car accidents tend to spike during the winter months each year in Iowa, as do costly delays. And dangerous commutes can mean hazards for people and commerce alike: the state is one of the country’s largest producers of agricultural output, and much of that is moved on roads.

To improve road safety and efficiency, the Iowa Department of Transportation has teamed up with researchers at Iowa State University to use machine learning, including our TensorFlow framework, to provide insights into traffic behavior. Iowa State’s technology helps analyze the visual data gathered from stationary cameras and cameras mounted on snow plows. They also capture traffic information using radar detectors. Machine learning transforms that data into conclusions about road conditions, like identifying congestion and getting first responders to the scenes of accidents faster..

This is just one recent example of TensorFlow being used to make drivers’ lives easier across the United States. In California, snow may not be an issue, but traffic certainly is, and college students there used TensorFlow to identify pot holes and dangerous road cracks in Los Angeles.

Officials in Iowa say machine learning could also be used to predict crash risks and travel speeds, and better understand drivers’ reactions or failures behind the wheel. But that doesn’t mean drivers will be off the hook. Iowa’s transportation and public safety departments constantly spread the same message: when it’s winter, slow down. Add some time onto your daily commute, and don’t use cruise control during a storm. That way, both drivers and state officials can work together to make winter travel less dreary—and a lot safer.

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