PERFIL DEL ALUMNO.

Before the regression analysis, thematic analysis was performed to investigate patterns and clusters of person-, vehicle-, and crash-level variables used in the injury severity analysis. This analysis was also found useful to get a general understanding of how variables

would/should perform. Furthermore, this also helped reduce and remove possibly inconsistent or redundant variables from consideration. Characteristics were classified into relatively

homogeneous clusters and later modeled using regression analysis. The groupings of variable clusters for the injury severity analysis are shown in Table 21 of appendix B.

The occupant injury regression focused on characteristics of person-, vehicle-, and crash- circumstantial attributes influencing the injury status of occupants involved in crashes with bridges on county roadways. The regression analysis was also conducted using NLOGIT 5 statistical software. Table 18 shows the ordered probabilistic regression results of occupant injury status for crashes involving bridges on Iowa’s county road network.

The results suggested five person-level, three vehicle-level, and three crash-level attributes that are statistically correlated with the ordinal ranking of injuries sustained by occupants of vehicle involved in collisions with bridges on county roadways. In order of appearance, those included the age of occupant, seat protection usage, front seat occupancy, occupant ejection, airbag deployment, vehicle truck configuration, number of vehicle occupants, initial frontal vehicle impact, wet, icy, snowy, or slushy surface condition, drug or alcohol- related incident, and work zone-related incident.

Similar to the crash frequency results, majority of the explanatory variables included in the regression model were indicator (dummy) variables as previously described. Those variables are also highlighted in yellow. Correspondingly, the associated test-statistic (Student’s t-statistic)

acknowledges that some attributes has significantly stronger explanatory properties than others, as indicated by the quantity of asterisks. The significance of the parameter thresholds implies that the ordinal scaling of the vehicle occupancy injury status confirms to be statistically different from one another. Based on the model results, major findings in regard to the probability of vehicle occupancy injury status are as follows.

Table 18: The random effects ordered probability model results of occupant injury severity (Dependent variable responses are intergers between 1 [No injury] and 5 [Fatal injury])

Explanatory Variable Variable Classification Coefficient test-statistic Significance

OCCU_AGE Person Level 0.00792 2.51 **

PROTECT Person Level -0.94764 -4.79 *** FRNTSEAT Person Level -0.56460 -1.70 *

EJECT Person Level 0.92275 1.74 *

AIRBAG Person Level 0.46089 4.32 ***

TRUCK Vehicle Level -1.06028 -1.77 *

OCCUPANT Vehicle Level 0.09041 1.39 IMPFRONT Vehicle Level -0.50261 -3.38 ***

WISS Crash Level -0.30982 -2.59 ***

DRUG_ALC Crash Level 0.50160 3.02 ***

WZRELATE Crash Level -0.81753 -1.28

Constant: −(𝜇0) 1.34286 3.26 *** Mu 01: (𝜇1) 0.66075 9.68 *** Mu 02: (𝜇2) 1.66239 11.81 *** Mu 03: (𝜇3) 2.39213 11.43 *** Number of Observations 819 Log-likelihood at zero -1279.52948 Log-likelihood at convergence -623.14423 Goodness-of-Fit (𝜌2_{) 0.51299}

Note: ***, **, * ==> Significance at 1%, 5%, 10% level.

1) It was observed that the age of an occupant played a significant role on level of injury sustained. The marginal effects (Table 22 of Appendix B) show that a unit increase in the age of a vehicle occupant reduces the probability of no injury by 0.00316. This implies that the probability of other injury categories (possible/unknown, minor, major, and fatal injury) all increase with age. However, the net effect of increase in age appears ambiguous for major and fatal injuries because of their reduced effects.

2) It was observed that the indicator variable for occupant protection (i.e. seat belt use) played a significant role on the level of injury sustained. The marginal effects show that seat belt use for instance increases the probability of no injury by 0.33671.

3) The indicator variable for seating position (i.e. front seat occupant) was observed to have an overall significant effect on level of injury sustained. The marginal effects show that front seat usage increases the probability of no injury by 0.21386. However, the net effect of front seat occupancy appears ambiguous for major and fatal injuries.

4) The indicator variable for occupant ejection was observed to have an overall significant effect on level of injury sustained. Its marginal effect shows reduction in probability of no injury by 0.32303. However, the net effect of ejection appears ambiguous for major and fatal injuries.

5) The indicator variable for airbag deployment was observed to have significant effect on level of injury sustained. Its marginal effect shows reduction in probability of no injury by 0.18196. Nonetheless, this seems prevailing for nonincapacitating injuries than otherwise. 6) The indicator variable for vehicular configuration was observed to have an overall

significant effect on level of injury sustained. The marginal effects show that truck vehicle configuration (as supposed to passenger car or motorcycle) increases the probability of no injury by 0.35887. However, it should be noted that the data represented little-to-no observations of motorcycle crashes.

7) It was observed that the total number of vehicle occupants had no significant effect on level of injury sustained. Nonetheless, a one unit increase in occupancy reduces the probability of a no injury by 0.03607.

8) The indicator variable for vehicle location of impact was observed to have significant effect on level of injury sustained. The marginal effects show that frontal impact increases the probability of no injury by 0.19528. However, the net effect of frontal impact appears ambiguous for major and fatal injuries.

9) The indicator variable for surface condition was observed to have significant effect on level of injury sustained. The marginal effects show that wet, icy, snowy, or slushy surface conditions increases the probability of no injury by 0.12304.

10) The indicator variable for driver condition was observed to have significant effect on level of injury sustained. The marginal effects show that driving under the influence of drug or alcohol reduces the probability of no injury by 0.19413.

11) Lastly, the indicator variable for (work zone) environment was observed to have no significant effect on level of injury sustained. Nonetheless, the marginal effects show that work zone increases the probability of no injury by 0.29480. Additionally, it should be noted that the data represented little-to-no observations of work zone related events.

Overall, the goodness-of-fit statistic 𝜌2 _{of 0.513 for the vehicle occupant injury severity}

model suggests the appropriate overall fit of the severity dataset results given its distribution. Specifically, the occupant injury severity results suggest evidence in support of an ordinal probabilistic regression. Other variations of ordinal regression, including ordered-logistic or nested-logit models could show comparable results, but those weren’t included in this analysis.

In addition to the marginal effects for the injury severity model results (Table 22 of appendix B), Table 23 shows a summary of the parameters elasticities which measure the effect of a 1% change in parameter (X) on the dependent variable (injury severity).

It should be advised that given the unbalanced sample distribution of observations, there are some limitations to the severity model results. Those include:

 Results of the occupant severity model are independent of bridge characteristics.  Unbalanced sampling of rear seat occupancy; less than 4% of vehicle occupants were

rear-seat users.

 Not all vehicles have or require rear/side safety devices.

 By state law, rear-seat occupants are not required to use/wear protective devices such as seat belts.

 Not all age groups are represented; specifically, lower age groups are naturally limited to rear-seat occupancy which may influence severity outcome.

 Airbag deployment is suggestive of more severe crashes at higher speeds.

 2 observations were characterized as motorcycle occupants (less than half a percent).  7 observations were characterized as work zone-related crashes (less than 1%).  Occupant ejection was not applicable to motorcyclists.

 Total ejection and protective device usage may be complementary although partial ejection may not.