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

Introduction

• General safety background• Description of the research method and crash data

• Illustration of the results• Discussion and Conclusions

General Safety Background

Road traffic crashes: Huge burden

Pictures from: www.nhtsa.com and www.images.google.com

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}

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

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

Research Method and Data Description

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)

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.

Multinomial Logit Regression Model

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.

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)

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.)• ……

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.)

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.)

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

Description of Research Results

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.

Results of Single Vehicle Model

Note: *- represents the parameters are statistically significant

Results of Car-Car Collision Model

Note: *- represents the parameters are statistically significant

Results of Car-Truck Collision Model

Note: *- represents the parameters are statistically significant

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.

Conclusion

• General safety background• Description of the research method and crash data

• Illustration of the results• Discussion

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