A Statistical Analysis of Recent Traffic Crashes in Massachusetts

TL;DR

This study uses statistical methods and machine learning to analyze Massachusetts traffic crash data, identifying key factors like weather, driver age, and intersection danger that increase injury risk.

Contribution

It introduces a binary classification model using logistic regression with feature selection to identify injury-causing crash circumstances.

Findings

Weather and road conditions significantly increase injury risk.

Senior and teen drivers are more involved in injury crashes.

Dangerous intersections are major injury risk factors.

Abstract

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.