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Machine Learning Tools for Informing Transportation Technology and Policy [Research Brief]
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2019-11-01
[PDF-392.52 KB]
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Abstract:Little work has been conducted to study practitioner induced subjectivities introduced into machine learning applications, which is important in understanding causes, influences, and methods for avoidance, particularly in transportation settings. To help fill this gap, this study uses two transportation datasets to examine car and pedestrian crash fatalities, deploying two different machine learning techniques of low and high complexity (logistic regression and neural networks).
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