A Statistical Analysis of Recent Traffic Crashes in Massachusetts
A statistical analysis implemented in the Python programming language was performed on the available MassDOT car accident data to identify whether a certain set of traffic circumstances would increase the likelihood of injuries. In the analysis, we created a binary classifier as a model to separate...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
19.10.2019
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Subjects | |
Online Access | Get full text |
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Summary: | A statistical analysis implemented in the Python programming language was
performed on the available MassDOT car accident data to identify whether a
certain set of traffic circumstances would increase the likelihood of injuries.
In the analysis, we created a binary classifier as a model to separate crashes
that resulted in injury from those that did not. To accomplish this, we first
cleaned up the initial data, then proceeded to represent categorical variables
numerically through one hot encoding before finally producing models with
Recursive Feature Elimination (RFE) and without RFE, in conjunction with
logistic regression. This statistical analysis plays a significant role in our
modern road network that has presented us with a heap of obstacles, one of the
most critical being the issue of how we can ensure the safety of all drivers
and passengers. Findings from our analysis identify that tough weather and road
conditions, senior/teen drivers and dangerous intersections play prominent
roles in accidents that resulted in injuries in Massachusetts. These new
findings can provide valuable references and scientific data support to
relevant authorities and policy makers for upgrading road infrastructure,
passing regulations, etc. |
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DOI: | 10.48550/arxiv.1911.02647 |