Abstract
Road closures due to adverse and severe weather continue to affect Wyoming due to hazardous driving conditions and temporarily suspending interstate commerce. The mountain ranges and elevation in Wyoming makes generating accurate predictions challenging, both from a meteorological and machine learning stand point. In a continuation of prior research, we investigate the 80 km stretch of Interstate-80 between Laramie and Cheyenne using autonomous machine learning to create an improved model that yields a 10% increase in closure prediction accuracy. We explore both serial and parallel implementations run on a supercomputer. We apply auto-sklearn, a popular and well documented autonomous machine learning toolkit, to generate a model utilizing ensemble learning. In the previous study, we applied a linear support vector machine with ensemble learning. We will compare our new found results to previous results.
This research was supported in part by National Science Foundation grant DMS-1722621.
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Carper, C., McClellan, A., Douglas, C.C. (2021). I-80 Closures: An Autonomous Machine Learning Approach. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_22
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DOI: https://doi.org/10.1007/978-3-030-77977-1_22
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