This document summarizes a student's analysis of the 2015 Traffic Fatalities dataset released by the National Highway Traffic Safety Administration. The dataset contains 15 tables of information on traffic accidents, vehicles, people involved, and other details. The student cleaned the data, created visualizations, and applied classifiers to predict things like drunk driving accidents based on month, day and hour. Their work is published on Kaggle where they are currently the 3rd top contributor. The student is seeking feedback and ideas for further analysis, such as fusing multiple tables, identifying gender bias or distraction effects, and clustering pedestrian fatalities to find high-risk regions.

1. Visualization and Analysis of
2015 Traffic Fatalities
Tahmid Abtahi ,MS Student
Computer Engineering, UMBC
abtahi1@umbc.edu

2. Dataset -2015 Traffic Fatalities Data
• Annually Released by National Highway Traffic Safety Administration
(NHTSA)
• White House and U.S. Department of Transportation’s Blog calling for data
scientists, students and researcher to provide analysis on this data
• Significance
– Dept of Transportation aggressively seeking insights to improve road safety
– Shaping Auto industry to improve vehicle safety
– Identifying communities at higher risk of fatal crashes etc.
– Insight to seek solutions to behavioral challenges like drunk, drugged, distracted
and drowsy driving

3. Components of Data Set
15 Tables
accident.csv – Crash data (State, county, Day, Hour, drunk driver, fatalities etc.)
cevent.csv - Qualifying events
damage.csv - damaged area of vehicle
distract.csv - driver distraction
drimpair.csv – physical impairment
nmimpair.csv – physical impairment of people not in vehicles
nmprior.csv - actions of non occupant people
parkwork.csv - parked and working vehicles involved
pbtype.csv – Crashes between motor and pedestrians, bicyclist
person.csv - Person data file (age-sex-injury severity-air bag etc)
vehicle.csv - Vehicle data type (number of occupants, model, make, registration
state etc.)
vevent.csv - sequence of event
vindecode.csv
vision.csv - circumstances which obscured driver vision

4. Approaches
• Data Clean up
• Visualization
• Applying classifiers
Results

5. Results

6. Results
Given Month, Day and Hour - predicting a drunk driving related accident ?

7. Next Ideas
• Fuse multiple tables
• Gender Bias on accident over state region
• Distraction effects on fatalities
• Clustering of pedestrian fatalities to identify potential risk regions
Source codes & visualizations in Kaggle kernels. Currently 3rd in the Top Contributor

8. Questions?