Using Data Mining and Using Data Mining and Bootstrapping to Develop Simple Bootstrapping to Develop Simple Models for Obtaining Confidence Models for Obtaining Confidence Intervals for the Percentage of Intervals for the Percentage of

Alcohol Related CrashesAlcohol Related Crashes

Using Data Mining and Using Data Mining and Bootstrapping to Develop Simple Bootstrapping to Develop Simple Models for Obtaining Confidence Models for Obtaining Confidence Intervals for the Percentage of Intervals for the Percentage of

Alcohol Related CrashesAlcohol Related Crashes

Joni Nunnery and Helmut Schneider Joni Nunnery and Helmut Schneider

Why Data Mining?• NHTSA Estimate is for the USA• State estimates are not readily available• Need for reliable standard errors for states

– 0.3% for USA 2% for LA• State estimate may be effected by local variab

les• Non-crash independent variables may change

over time – DWI versus pretrial diversion

• IM estimates complicated statistical technique• Data Mining tools are used in various

applications

Approach• Analysis of Louisiana Crash Data

1999-2002• Data mining model is used to

predict alcohol involvement• Estimation of standard error via

bootstrap type simulation

KNOWN ALCOHOL TESTS RESULTS

LOUISIANAN 1999-2002

  TEST RESULT  

POLICEREPORT

No Alcohol Alcohol

Total Row

NO ALCOHOL 953 62 1015

ALCOHOL 51 508 559

UNKNOWN 400 268 668

Total Column 1404 838 2242

ROW PERCENTAGES

  TEST RESULT  

POLICE REPORT

No Alcohol Alcohol

Total Row

NO ALCOHOL 94% 6% 100%

ALCOHOL 9% 91% 100%

UNKNOWN 60% 40% 100%

All Drivers in Crashes

Louisiana 1999-2002 TEST RESULT

POLICEREPORT

No Alcoho

lAlcoh

ol

Alcoholunknow

n

NOALCOHOL 953 62 1044 2059

ALCOHOL 51 508 170 729

UNKNOWN 400 268 896 1564

Total Column 1404 838 2110 4352

Using Insightful Miner Data Mining Software

Classification Models• Logistic Regression• Naive Bayes• Neural Network• Classification Tree

Classification Tree• Fit model to half the data• Tree model• What did we learn? –

– Importance of variables

Classification Results    Predicted Alcohol

Totals    No Yes

ObserveAlcohol

No 685 65 750

Yes 93 317 410

Totals   778 382 1160

         

    Observed   Overall

    No Yes  

  % Agree 91.30% 77.30% 86.40%

Violation

Hour of Day

Vehicle Type

Age

Injury

Parish

Number of Vehicles

Belt Usage

Day of Week

Gender

  Fatal CrashesEstimated-Alcohol Related

Fatalities

Year Crashes

AlcoholRelatedCrashes

PercentAlcoholRelate

CrashesFatalities

(LA)

PercentFatalities

(LA)

PercentFatalities

(US)

1996 806 414 51% 479 53% 41%

1997 833 384 46% 427 46% 39%

1998 807 371 46% 437 47% 38%

1999 831 382 46% 431 45% 38%

2000 846 403 48% 446 48% 40%

2001 859 389 45% 429 45% 41%

2002 785 375 48% 419 48% N/A

Alcohol in Injury and Property Damage

Crashes  Alcohol-Related Injury Crashes Property-Damage-Only Crashes

YearCrashes (1,000)

Alcohol-Related Crashes (1,000)

Percent Alcohol- Related Crashes

Crashes (1,000)

Alcohol-Related Crashes (1,000)

Percent Alcohol-Related Crashes

1996 52.1 4.2 8.10% 108 N/A N/A

1997 51.5 4 7.70% 112 3.8 3.40%

1998 47.1 3.5 7.50% 111 3.4 3.10%

1999 45.1 3.9 8.70% 107 4 3.80%

2000 48.3 4.3 8.90% 109 4 3.70%

2001 48.8 4.1 8.40% 110 4.1 3.70%

2002 50.9 5.4 10.50% 115 6.8 5.90%

Standard Error• Using simulation on second half of

data set to get estimated error• Evaluate combined standard error • The resulting standard error is 1%

for 900 crashes

Conclusion• Data mining is a simple and useful

tool to predict missing observations

• The best predictor for alcohol related crashes are the judgment of a well trained police officer on the scene

Alcohol-Related Crashes in Louisiana by Highway