Do Alcohol and Other Drugs Significantly Affect Traffic Accident Types?

1. Do Alcohol and Other Drugs Significantly Affect Traffic Accident Types? Trisha Muñoz, E.I.TCivil Engineering DepartmentCal Poly Pomona

2. Introduction • General safety background • Description of the research method and crash data • Illustration of the results • Discussion and Conclusions

3. General Safety Background

4. Road traffic crashes: Huge burden Pictures from: www.nhtsa.com and www.images.google.com

6. ROAD SAFETY STATUS 2005 } • Statistics (2005 – NHTSA Traffic Safety Facts) • Fatal 43,443 • Injury 2,699,000 • Property Damage Only 4,304,000 • Traffic Crash Victims Killed Injured • Occupants • Drivers 26,549 1,920,000 • Passengers 11,199 880,000 • Unknown 112 • Nonmotorists • Pedestrians 4,808 71,000 • Pedalcyclists 662 48,000 • Other/Unknown 113 7,000 7,046,443

7. NHTSA Facts of Impaired Driving • Impaired driving is often a symptom of a larger problem: alcohol misuse and abuse. • Alcohol-impaired motor vehicle crashes cost more than an estimated $37 billion annually. • In 2010, more than 10,000 people died in alcohol-impaired driving crashes - one every 51 minutes. Sources: http://www.nhtsa.gov/Impaired

8. Focus of the presentation • Numerous Past research studies : • driving performance is seriously impaired by alcohol and many other drugs. • However, very few research studies: • identifying the effects of alcohol and other drugs on traffic accident types • rear-end • head on • sideswipe • fixed object • others

9. Research Method and Data Description

10. Normal Linear Regression Requires Three strong assumptions • Normally distributed errors (i.e., residues) • Constant variance of errors • No relationships among the independent variables (i.e., regressor variables, or predictors)

11. In This Study • The dependent variable y has categorical nature (i.e., various accident types), which is not normally distributed • Therefore, the Normal Linear Regression is not appropriate herein. • Instead, we use Multinomial Logit Regression Model.

12. Multinomial Logit Regression Model

13. Arizona Crash Data • Road Sections from State Routes 77 and 83 in Tucson, AZ • Total mileage: 83 miles • Crash period: 6 years (Oct. 2003~ Sept. 2008) • Information: crash, driver, vehicle, environment, roadway, etc.

14. Crash Information • Date & time • Day of week • Crash location • Crash severity ( No Injury; Possible Injury; Non-incapacitating; Incapacitating; Fatal; unknown) • Collision type (rear-end, head-on, collision with fixed objects, etc.) • Hit-and-run (yes, no)

15. Driver(s) Information • Sex • Age • Conditions influencing drivers (use of illicit drugs; physical impairment, illness, etc.) • Violations (speed; made improper turn; ran stop sign, etc.) • ……

16. Vehicle Information • Number of vehicles • Vehicle condition: (No apparent defects; defective brakes; defective steering, etc.) • Vehicle type: (passenger cars, school bus, RVs, pick up trucks, etc.) • Vehicle action: (making left-turn, making U-turn, changing lanes, backing, etc.)

17. Roadway Information • Pavement material: ( concrete, asphalt, other) • Surface condition: (dry, wet, sand, ice, etc.) • Roadway defects • Roadway alignment-horizontal • Roadway alignment-vertical • Unusual roadway condition (no unusual conditions, under repair, under construction-traffic detoured, etc.) • Roadway characteristic (2-way striped median; 2-way painted median; 2-way raised median, etc.)

18. Environment Information • Location classification (recreational, farm, business, school, etc.) • Weather conditions (clear, not clear) • Light conditions (Daylight, others) • Traffic level (light, heavy& medium) • Speed limit

19. Description of Research Results

20. Some Notes of the Research Results • To improve modeling accuracy, 3 models were estimated separately for various accident types • single vehicle • car colliding with car • car colliding with trucks. • For the categorical accident types, level 1 (others) is used as the reference level. • For the categorical driver physical conditions, level 1( others and unknown) is used as the reference level.

21. Results of Single Vehicle Model Note: *- represents the parameters are statistically significant

22. Results of Car-Car Collision Model Note: *- represents the parameters are statistically significant

23. Results of Car-Truck Collision Model Note: *- represents the parameters are statistically significant

24. Discussion of Results • For all the three types of models (single vehicles, car-car collision, and car-truck collision), the use of alcohol significantly affects the accident types. • However, the use of illicit drug and other physical conditions has not shown an apparent influence to the types. • Since the research study uses only the accident data from the State of Arizona, the study findings need further confirmation.

25. Conclusion • General safety background • Description of the research method and crash data • Illustration of the results • Discussion

26. THANK YOU!