Traffic Fatalities 2015 - Visualization and Analysis
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
Given Month, Day and Hour - predicting a drunk
driving related accident ?
5. 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
