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
- 6. Questions?