The research described in this paper explored the factors contributing to injury severity resulting from rollover accidents of light trucks—SUVs and pickups in particular—in Alabama. Given the occurrence of a rollover crash, random parameter logit models of injury severity (with possible outcomes of fatal, major, minor, and possible or no injury) were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from SUV and pickup rollover crashes. According to these models, some variables were significant in one model (SUV or pickup) but not the other. For example, variables such as roadway downgrade, female drivers, and daylight were significant only in the SUV model. Variables such as driver fatigue, driver or occupant wearing a seat belt, and freeway were significant only in the pickup model. In addition, some variables (such as not wearing a seat belt, two-lane roadway, horizontal curve, and old driver) were significant in both models. Estimation findings showed that two parameters (horizontal curve and at intersections) in the SUV model and two parameters (horizontal curve and dry roadway surface) in the pickup model could be modeled as random parameters, indicating their varying influence on the injury severity related to unobserved effects. The results obtained are used in this paper for a discussion of the effects of variables on pedestrian and bicyclist injury severities and their possible explanations.
Get full access to this article
View all access options for this article.
References
1.
Traffic Safety Facts: 2012: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System. DOT HS 812 032. NHTSA, U.S. Department of Transportation, 2014.
2.
Characteristics of Fatal Rollover Crashes. DOT HS 809 438. NHTSA, U.S. Department of Transportation, 2002.
3.
An Analysis of Motor Vehicle Rollover Crashes and Injury Outcomes. DOT HS 810 741. NHTSA, U.S. Department of Transportation, 2007.
4.
WalzM. C.Trends in the Static Stability Factor of Passenger Cars, Light Trucks, and Vans. DOT HS 809 868. NHTSA, U.S. Department of Transportation, 2005.
5.
FarmerC. M., and LundA. K.. Rollover Risk of Cars and Light Trucks After Accounting for Driver and Environmental Factors. Accident Analysis and Prevention, Vol. 34, 2002, pp. 163–173.
6.
UlfarssonG. F., and ManneringF. L.. Differences in Male and Female Injury Severities in Sport-Utility Vehicle, Minivan, Pickup and Passenger Car Accidents. Accident Analysis and Prevention, Vol. 36, No. 2, 2004, pp. 135–147.
7.
ChenF., and ChenS.. Injury Severity of Truck Drivers in Single- and Multi-Vehicle Accidents on Rural Highways. Accident Analysis and Prevention, 43, 2011, pp. 1677–1688.
8.
IslamS., and ManneringF.. Driver Aging and Its Effect on Male and Female Single-Vehicle Accident Injuries: Some Additional Evidence. Journal of Safety Research, Vol. 37, No. 3, 2006, pp. 267–276.
9.
IslamS., JonesS. L., and DyeD.. Comprehensive Analysis of Single- and Multi-Vehicle Large Truck At-Fault Crashes on Rural and Urban Roadways in Alabama. Accident Analysis and Prevention, Vol. 67, 2014, pp. 148–158.
10.
MiltonJ., ShankarV., and ManneringF.. Highway Accident Severities and the Mixed Logit Model: An Exploratory Empirical Analysis. Accident Analysis and Prevention, Vol. 40, No. 1, 2008, pp. 260–266.
11.
AnastasopoulosP., and ManneringF.. An Empirical Assessment of Fixed and Random Parameter Logit Models Using Crash- and Non-Crash-Specific Injury Data. Accident Analysis and Prevention, Vol. 43, No. 3, 2011, pp. 1140–1147.
12.
MorganA., and ManneringF.. The Effects of Road-Surface Conditions, Age, and Gender on Driver-Injury Severities. Accident Analysis and Prevention, Vol. 43, No. 5, 2011, pp. 1852–1863.
13.
DuncanC., KhattakA., and CouncilF.. Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions. In Transportation Research Record 1635, TRB, National Research Council, Washington, D.C., 1998, pp. 63–71.
14.
ManneringF. L., and BhatC. R.. Analytic Methods in Accident Research: Methodological Frontier and Future Directions. Analytic Methods in Accident Research, Vol. 1, 2014, pp. 1–24.
15.
YamamotoT., and ShankarV.. Bivariate Ordered-Response Probit Model of Driver’s and Passenger’s Injury Severities in Collisions with Fixed Objects. Accident Analysis and Prevention, Vol. 36, No. 5, 2004, pp. 869–876.
16.
SavolainenP., ManneringF., LordD., and QuddusM.. The Statistical Analysis of Crash-Injury Severities: A Review and Assessment of Methodological Alternatives. Accident Analysis and Prevention, Vol. 43, No. 5, 2011, pp. 1666–1676.
17.
KhorashadiA., NiemeierD., ShankarV., and ManneringF.. Differences in Rural and Urban Driver–Injury Severities in Accidents Involving Large Trucks: An Exploratory Analysis. Accident Analysis and Prevention, Vol. 37, No. 5, 2005, pp. 910–921.
18.
KimJ.-K., UlfarssonG. F., KimS., and ShankarV. N.. Driver-Injury Severity in Single-Vehicle Crashes in California: A Mixed Logit Analysis of Heterogeneity Due to Age and Gender. Accident Analysis and Prevention, Vol. 50, 2013, pp. 1073–1081.
19.
YasminS., and EluruN.. Evaluating Alternate Discrete Outcome Frameworks for Modeling Crash Injury Severity. Accident Analysis and Prevention, Vol. 59, No. 1, 2013, pp. 506–521.
20.
WashingtonS. P., KarlaftisM. G., and ManneringF. L.. Statistical and Econometric Methods for Transportation Data Analysis (2nd ed.), Chapman & Hall/CRC, Boca Raton, Fla., 2011.
21.
MooradianJ., IvanJ. N., RavishankarN., and HuS.. Analysis of Driver and Passenger Crash Severity Using Partial Proportional Odds Models. Accident Analysis and Prevention, Vol. 58, 2013, pp. 53–58.
22.
McFaddenD.Econometric Models of Probabilistic Choice. In A Structural Analysis of Discrete Data with Econometric Applications (ManskiC. F. and McFaddenD. L., eds.), MIT Press, Cambridge, Mass., 1981.
23.
TrainK.Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge, United Kingdom, 2003.
24.
McFaddenD., and TrainK.. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics, Vol. 15, No. 5, 2000, pp. 447–470.
25.
HaltonJ.On the Efficiency of Evaluating Certain Quasi-Random Sequences of Points in Evaluating Multi-Dimensional Integrals. Numerische Mathematik, Vol. 2, No. 1, 1960, pp. 84–90.
26.
BhatC.Simulation Estimation of Mixed Discrete Choice Models Using Randomized and Scrambled Halton Sequences. Transportation Research Part B, Vol. 37, No. 1, 2003, pp. 837–855.
27.
HensherD., LouviereJ., and SwaitJ.. Combining Sources of Preference Data. Journal of Econometrics, Vol. 89, 1999, pp. 197–221.
28.
GkritzaK., and ManneringF. L.. Mixed Logit Analysis of Safety-Belt Use in Single- and Multi-Occupant Vehicles. Accident Analysis and Prevention, Vol. 40, 2008, pp. 443–451.
29.
UlfarssonG. F., KimS., and BoothK. M.. Analyzing Fault in Pedestrian–Motor Vehicle Crashes in North Carolina. Accident Analysis and Prevention, Vol. 42, No. 6, 2010, pp. 1805–1813.
30.
IslamS., and JonesS. L.. Pedestrian At-Fault Crashes on Rural and Urban Roadways in Alabama. Accident Analysis and Prevention, Vol. 72, 2014, pp. 267–276.
31.
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.
32.
Countermeasures That Work: A Highway Safety Countermeasure Guide for State Highway Safety Offices (7th ed.). DOT HS 811 727. NHTSA, U.S. Department of Transportation, 2013.
33.
ZegeerC., StewardR., ReinfutD., CouncilF., NeumanT., HamiltonE., MillerT., and HunterW.. Cost-Effective Geometric Improvements for Safety Upgrading of Horizontal Curves, Vol. 1. FHWA, U.S. Department of Transportation, 1990.
34.
KimJ.-K., UlfarssonG. F., ShankarV. N., and ManneringF. L.. A Note on Modeling Pedestrian Injury Severity in Motor-Vehicle Crashes with the Mixed Logit Model. Accident Analysis and Prevention, Vol. 42, 2010, pp. 1751–1758.
35.
AzizH. M. A., UkkusuriS., and HasanS.. Exploring the Determinants of Pedestrian–Vehicle Crash Severity in New York City. Accident Analysis and Prevention, Vol. 50, 2013, pp. 1298–1309.
