Abstract
The US DoT estimates 22% of the 5.7 million vehicle crashes a year are weather related. At Idaho National Laboratories, home of the DOE’s largest transit, heavy and light vehicle fleet in the nation, weather is a constant challenge for the 4000 employees traveling the 45 to 65 mile stretch of road. Driving conditions can vary immensely; micro-climate conditions at INL site locations highways go unmonitored and causing severe challenges. INL has taken the initiative to review applicable technologies determining that addressing severe weather and road conditions through the application of advanced modeling methods holds promise for enhancing driver safety and dispatch planning. INL engaged IBM Global Business Services Advanced Analytics Center of Competency (CoC) Team for support in this effort. This presentation reviews the benefits expected, data surveyed, and how to use integrated sources and cognitive analytics to improve real-time weather forecasting and INL site fleet and operations planning.
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Acknowledgements
The authors acknowledge Daniel Yawitz and Reid Mechanick for their knowledge, technical expertise, and invaluable contributions to the creation of the analytical models and visualization platform.
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Spielman, Z. et al. (2018). Machine Learning and Big Data Analytics in Support of Fleet Safety During Severe Weather. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2017. Advances in Intelligent Systems and Computing, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-60441-1_64
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DOI: https://doi.org/10.1007/978-3-319-60441-1_64
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