Session 13: \u3cem\u3ePredicting Automobile Accident Severity and Hotspots Using Multinomial Logistic regression\u3c/em\u3e

Americans are now driving more than ever [1]. In 2010, close to 33,000 lives were lost and another estimated 3.9 million people were injured in automobile accidents; all things considered, these accidents accounted for $836 billion in damages [2]. Since then, the rate of automobile-related deaths per 100 million miles traveled has not shown signs of improvement [3]. This research expands upon a previous year’s poster presented at the South Dakota State University Data Science Symposium 2019 [4]. While the previous research focuses on a data visualization of automobile accident hotspots on a map based on the severity and frequency of accidents, this research aims to train a multinomial logistic regression machine learning model using data related to weather conditions, speed limit, and GPS coordinates to predict the severity of automobile accidents. The development of such a machine learning model can help inform emergency services better manage resources in anticipation of potential automobile accidents based on prevailing weather conditions, speed limit along a stretch of road, and location data. An updated version of the previous dataset will be used. This dataset contains approximately 1.5 million automobile accident data points, collected over a span of over four years, from February 2016 to December 2020