An emerging copula-based methodology is used to address endogeneity in models of crash injury severity. Specifically, two important sources of endogeneity are addressed: (a) endogeneity due to correlations between the injury severity of the two drivers involved in two-vehicle crashes and (b) endogeneity of collision type and injury severity outcomes. Two sets of copula-based joint model systems are formulated and estimated by using data on two-vehicle crashes from the 2007 Generalized Estimates System: a copula-based joint ordered logit–ordered logit model of injury severities of the two drivers involved in two-vehicle crashes and a copula-based joint multinomial logit–ordered logit model of collision type and injury severity outcomes of two-vehicle crashes. The model estimation results and elasticity estimates underscore the importance of accommodating endogeneity in models of crash injury severity. The results shed new light on the determinants of injury severity in two-vehicle accidents.
Get full access to this article
View all access options for this article.
References
1.
WashingtonS., KarlaftisM., and ManneringF.Statistical and Econometric Methods for Transportation Data Analysis.Chapman and Hall, Boca Raton, Fla., 2003.
2.
EvansL.Safety-Belt Effectiveness: The Influence of Crash Severity and Selective Recruitment. Accident Analysis and Prevention, Vol. 28, No. 4, 1996, pp. 423–433.
3.
EluruN., and BhatC. R.A Joint Econometric Analysis of Seat Belt Use and Crash Related Injury Severity. Accident Analysis and Prevention, Vol. 39, No. 5, 2007, pp. 1037–1049.
4.
DeeT. S.Reconsidering the Effects of Seat Belts Laws and their Enforcement Status. Accident Analysis and Prevention, Vol. 30, No. 1, 1997, pp. 1–10.
5.
DerrigR. A., Segui-GomezM., AbtahiA., and LiuL. L.The Effect of Population Safety Belt Usage Rates on Motor Vehicle-Related Fatalities. Accident Analysis and Prevention, Vol. 34, No. 1, 2000, pp. 101–110.
6.
CohenA., and EinavL.The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities. Review of Economics and Statistics, Vol. 85, No. 4, 2003, pp. 828–843.
7.
de LapparentM.Willingness to Use Safety Belt and Levels of Injury in Car Accidents. Accident Analysis and Prevention, Vol. 40, No. 3, 2008, pp. 1023–1032.
8.
KimK., NitzL., RichardsonJ., and LiL.Personal and Behavioral Predictors of Automobile Crash and Injury Severity. Accident Analysis and Prevention, Vol. 27, No. 4, 1995, pp. 469–481.
9.
YeX., PendyalaR. M., Al-RukaibiF. S., and KonduriK.A Joint Model of Accident Type and Severity for Two-Vehicle Crashes. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
10.
LeeC., and Abdel-AtyM.Presence of Passengers: Does It Increase or Reduce Driver's Crash Potential?Accident Analysis and Prevention, Vol. 40, No. 5, 2008, pp. 1703–1712.
11.
KimD. G., and WashingtonS.The Significance of Endogeneity Problems in Crash Models: An Examination of Left-Turn Lanes in Intersection Crash Models. Accident Analysis and Prevention, Vol. 38, No. 6, 2006, pp. 125–134.
12.
HutchinsonT. P.A Bivariate Normal Model for Intra-Accident Correlations of Driver Injury with Application to the Effect of Mass Ratio. Accident Analysis and Prevention, Vol. 15, No. 3, 1983, pp. 215–224.
13.
HutchinsonT. P.Statistical Modeling of Injury Severity, with Special Reference to Driver and Front Seat Passenger in Single-Vehicle Crashes. Accident Analysis and Prevention, Vol. 18, No. 2, 1986, pp. 157–167.
14.
OuyangY., YamamotoT., and ShankarV.Modeling the Simultaneity in Injury Causation in Multivehicle Collisions. In Transportation Research Record: Journal of the Transportation Research Board, No. 1784, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 143–152.
15.
YamamotoT., and ShankarV. N.Bivariate Ordered-Response Probit Model of Driver's and Passenger's Injury Severities in Collision with Fixed Objects. Accident Analysis and Prevention, Vol. 36, No. 5, 2004, pp. 869–876.
16.
JonesA. P., and JorgensenS. H.The Use of Multilevel Models for the Prediction of Road Accident Outcomes. Accident Analysis and Prevention, Vol. 35, No. 1, 2003, pp. 59–69.
17.
LenguerrandE., MartinJ. L., and LaumonB.Modeling the Hierarchical Structure of Road Crash Data: Application to Severity Analysis. Accident Analysis and Prevention, Vol. 38, No. 1, 2006, pp. 43–53.
18.
KimD. G., LeeY., WashingtonS., and ChoiK.Modeling Crash Outcome Probabilities at Rural Intersections: Application of Hierarchical Binomial Logistic Models. Accident Analysis and Prevention, Vol. 39, No. 1, 2007, pp. 1094–1100.
19.
HelaiH., ChorC. H., and HaqueM. M.Severity of Driver Injury and Vehicle Damage in Traffic Crashes at Intersections: A Bayesian Hierarchical Analysis. Accident Analysis and Prevention, Vol. 40, No. 1, 2008, pp. 45–54.
20.
NelsonR. B.An Introduction to Copulas, 2nd ed. Springer-Verlag, New York, 2006.
21.
BhatC. R., and EluruN.A Copula-Based Approach to Accommodate Residential Self-Selection Effects in Travel Behavior Modeling. Transportation Research Part B, Vol. 43, No. 7, 2009, pp. 749–765.
22.
SklarA.Random Variables, Joint Distribution Functions, and Copulas. Kybernetika, Vol. 9, 1973, pp. 449–460.
23.
SpissuE., PinjariA. R., PendyalaR. M., and BhatC. R.A Copula-Based Joint Multinomial Discrete-Continuous Model of Vehicle Type Choice and Miles of Travel. Transportation, Vol. 36, No. 4, 2009, pp. 403–422.
