Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange - SDSU Data Science Symposium: Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression
 

Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Zhuoyu Yang, Minot State University

Abstract

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.

References

[1] US Department of Transportation. Federal Highway Administration. (May, 2019). Strong economy has Americans driving more than ever before. Retrieved from https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever

https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever

[2] Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May). The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration

[3] National Center for Statistics and Analysis (2019, December). Early estimate of motor vehicle traffic fatalities for the first 9 months (Jan – Sep) of 2019. (Crash Stats Brief Statistical Summary. Report No. DOT HS 812 874). Washington, DC: Highway Traffic Safety Administration.

[4] Identification of Automobile Accident Hotspots using Countrywide Traffic Accident Dataset, B. Z. Yang & S. Z. Sajal, Ph.D., Presented at 2020 South Dakota State University Data Science Symposium. Public Affairs, Public Policy and Public Administration

 
Feb 8th, 1:00 PM

Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Volstorff A

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.

References

[1] US Department of Transportation. Federal Highway Administration. (May, 2019). Strong economy has Americans driving more than ever before. Retrieved from https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever

https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever

[2] Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May). The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration

[3] National Center for Statistics and Analysis (2019, December). Early estimate of motor vehicle traffic fatalities for the first 9 months (Jan – Sep) of 2019. (Crash Stats Brief Statistical Summary. Report No. DOT HS 812 874). Washington, DC: Highway Traffic Safety Administration.

[4] Identification of Automobile Accident Hotspots using Countrywide Traffic Accident Dataset, B. Z. Yang & S. Z. Sajal, Ph.D., Presented at 2020 South Dakota State University Data Science Symposium. Public Affairs, Public Policy and Public Administration