Potential Crash Involvement of Young Novice Drivers with Previous Crash and Citation Records

Abstract

A goal for any licensing agency is the ability to identify crash-prone drivers. Thus, the objective of this study is the development of a crash prediction model that can be used to estimate the likelihood of a young novice driver's involvement in a crash occurrence. Multiple logistic regression techniques were employed with available Kentucky data. This study considers as crash predictors the driver's total number of previous crashes, citations accumulated, and demographic factors. The driver's total number of previous crashes was further disaggregated into the driver's total number of previous at-fault and not-at-fault crashes. Sensitivity analysis was used to select an optimal cut-point for the model. The overall efficiency of the model is 77.82%, and it can be used to classify correctly more than one-third of potential crash-prone drivers if a cut-point of 0.247 is selected. The total number of previous at-fault and not-at-fault crash involvements and the accumulation of speeding citations are strongly associated with a driver's being at risk. In addition, a driver's risk is increased by being young and being male. Although the statistical nature of driver crash involvements makes them difficult to predict accurately, the model presented here enables agencies to identify correctly 49.4% of crash-involved drivers from the top 500 high-risk drivers. Moreover, the model can be used for driver control programs aimed at road crash prevention that may range from issuance of warning letters to license suspension.

Get full access to this article

View all access options for this article.

References

Williams

A. F.

Graduated Licensing Comes to the United States.

Injury Prevention, Vol. 5, No. 2, 1999, pp. 133–135.

Elliott

M. R.

, Waller

P. F.

, Raghunathan

T. E.

, and Shope

J. T.

. Predicting Offenses and Crashes from Young Drivers’ Offence and Crash Histories. Journal of Crash Prevention and Injury Control, Vol. 2, No. 3, 2001, pp. 167–178.

Kirk

, and Stamatiadis

. Crash Rates and Traffic Maneuvers of Young Drivers. In Transportation Research Record: Journal of the Transportation Research Board, No. 1779, TRB, National Research Council, Washington, D.C., 2001, pp. 68–74.

McKnight

J. A.

, and McKnight

A. S.

. Young Novice Drivers: Careless or Clueless? Accident Analysis & Prevention, Vol. 35, No. 6, 2003, pp. 921–925.

Harrison

W. A.

Investigation of the Driving Experience of a Sample of Victorian Learner Drivers.

Accident Analysis & Prevention, Vol. 36, No. 5, 2004, pp. 885–891.

Twisk

D. A. M.

Young Driver Accidents in Europe: Magnitude and Nature. In New to the Road: Reducing the Risks for Young Motorists ( Simpson

, ed.), Youth Enhancement Service, UCLA School of Medicine, Los Angeles, Calif., 1996, pp. 27–33.

Kentucky Traffic Collision Facts. Kentucky Transportation Center, College of Engineering, University of Kentucky,

Lexington,

2002.

Put the Brakes on Fatalities Day. FHWA, U.S. Department of Transportation. http://safety.fhwa.dot.gov/fourthlevel/brakes_facts.htm. Accessed Oct. 13, 2003.

Blasco

R. D.

, Prieto

J. M.

, and Cornejo

J. M.

. Accident Probability After Accident Occurrence. Safety Science, Vol. 41, No. 6, 2003, pp. 481–501.

10.

Hauer

, Persaud

B. N.

, Smiley

, and Duncan

. Estimating the Accident Potential of an Ontario Driver. Accident Analysis & Prevention. Vol. 23, No. 2/3, 1991, pp. 133–152.

11.

Peck

R. C.

, and Coppin

R. S.

. The Prediction of Accident Involvement Using Concurrent Driver Record Data. Research Review (National Safety Council), Vol. 11, 1967, pp. 34–41.

12.

Gebers

M. A.

Strategies for Estimating Drive Accident Risk in Relation to California's Negligent-Operator Point System. Technical Monograph 183. California Department of Motor Vehicles, Research and Development Branch,

Sacramento,

1999.

13.

Gebers

M. A.

, and Peck

R. C.

. Using Traffic Conviction Correlates to Identify High Accident-Risk Drivers. Accident Analysis & Prevention, Vol. 35, 2003, pp. 903–912.

14.

Title XVI—Motor Vehicles. In

Kentucky Revised Statutes, Kentucky Legislature, 2003, Chaps. 186–189.

15.

Chen

, Cooper

, and Pinili

. Driver Accident Risk in Relation to the Penalty Point System in British Columbia. Accident Analysis & Prevention, Vol. 26, No. 1, 1995, pp. 9–18.

16.

Peck

R. C.

, McBride

R. S.

, and Coppin

R. S.

. The Distribution and Prediction of Driver Accident Frequencies. Accident Analysis & Prevention, Vol. 2, 1971, pp. 243–299.

17.

Stamatiadis

, and Deacon

J. A.

. Trends in Highway Safety: Effects of an Aging Population on Accident Propensity. Accident Analysis & Prevention, Vol. 27, No. 4, 1995, pp. 443–459.

18.

Johnson

D. E.

Applied Multivariate Methods for Data Analysts. Duxbury Press, Pacific Grove, Calif., 1998.

19.

Norusis

M. J.

SPSS Regression Models 10.0. SPSS, Inc.,

Chicago, Ill.,

1999.

20.

Masten

S. V.

, and Peck

R. C.

. Problem Driver Remediation: A Meta-Analysis of the Driver Improvement Literature. Insurance Institute for Highway Safety, Arlington, Va., 2003.

21.

Zweig

M. H.

, and Campbell

. Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine. Clinical Chemistry, Vol. 39, No. 4, 1993, pp. 561–577.