Document Type
Dissertation
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Information Science
Abstract
This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, the traditional momentum model’s severity results were not statistically significant and it was therefore impossible to say the SVM classifier was an improvement using the same data. The principle direction of force was improved on using both a multi-level SVM classifier and a multinomial regression and the results were statistically significant.
DOI
https://doi.org/10.21427/D74F8Z
Recommended Citation
Byrne, G. Can Machine Learning beat Physics at Modeling Car Crashes? Dissertation M.Sc. in Computing (Data Analytics), DIT, 2018.
Publication Details
A dissertation submitted in partial fulfillment of the requirements of Technological University Dublin for the degree of M.Sc. in Computing (Data Analytics) 2018.