Title

Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Presentation Type

Poster

Student

Yes

Track

Other

Abstract

Title: Predicting automobile accident severity and hotspots using multinomial logistic regression.

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

[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.

Start Date

2-8-2022 1:00 PM

End Date

2-8-2022 2:00 PM

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Feb 8th, 1:00 PM Feb 8th, 2:00 PM

Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Title: Predicting automobile accident severity and hotspots using multinomial logistic regression.

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

[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.