3.2.2. Heterogeneous Effects of the Contributory Factors
The study’s objective was to identify and analyze the heterogeneous effects of the contributory factors on the injury outcomes of interstate crashes involving large trucks. Therefore, we will keep our focus on the varying effects of the contributory factors on injury outcomes between the EDS and cluster-based models; the contributory factors with random parameters in all of the models; and the contributory factors that are exclusive to each of the cluster-based models.
Table 4,
Table 5,
Table 6 and
Table 7 show the coefficients, z statistics, average pseudo-elasticities, and the mean and variance of the random parameters for the statistically significant contributory factors. The random parameters of the contributory factors followed the normal distribution. The average pseudo-elasticities were computed using the “marginaleffects” package for R. The contributory factors shown in the tables were statistically significant at a 5% confidence level. In all of the models, the no-injury outcome was set as the baseline or reference level in the dependent variable. The majority of the independent contributory factors were of the categorical type in this study. The coefficients of the categorical factors indicate the changes in the probability of each injury severity level relative to the reference level of the categorical factors. The ρ2 of the models varied between 9% and around 18%. The number of observations, log-likelihood at zero, and log-likelihood at convergence are also displayed in the tables. The reference level of the binary categorical factors is “no” in all cases.
Table 4,
Table 5,
Table 6 and
Table 7 indicated that some of the statistically significant contributory factors were common between the EDS and the cluster-based models. The following factors were significant in both the EDS-based and cluster-based models: head-on collision, sideswipe collision, no collision, driving under the influence of alcohol or drugs (DUI), urban areas, overturning, not wearing a seatbelt, and vehicle count. However, the degree of their effects on injury outcomes differed across the models.
Head-on collisions are likely to increase the chances of serious injury in almost all scenarios of interstate crashes involving large trucks. The parameters of head-on collisions were fixed in all of the models. On the other hand, the positive impacts of head-on collisions on serious injuries were substantially higher in the CL1- and CL2-based models. Several studies have also indicated that head-on collisions are more likely to increase the chance of severe injuries in crashes involving large trucks [
2,
42,
43].
Sideswipe collisions are likely to reduce the chances of both non-severe and serious injuries under conditions such as those in the EDS and the clusters. However, the negative effects of sideswipe collisions on both non-severe and serious injuries were comparatively low in the CL2-based model. Behnood and Mannering [
13] have also reported that sideswipe collisions involving large trucks are likely to reduce the chances of severe injuries. The results also indicated that the parameters of sideswipe collisions for serious injury function varied significantly in all the models. In the majority of cases, sideswipe collisions are less likely to lead to serious injuries. However, the mean and standard deviation of the parameters of sideswipe collisions for serious injury function indicated that 12.51%, 23.28%, 15.17%, and 10.05% of the sideswipe collisions involving large trucks on interstate roadways are more likely to experience serious injuries under conditions similar to EDS, CL1, CL2, and CL3, respectively. The parameters of sideswipe collisions for non-severe injury function in the CL1-based model also varied across the observations. A total of 25.47% of the sideswipe collisions in CL2 are more likely to sustain non-severe injuries. One reasonable explanation for sideswipe collisions having random parameters is that they can occur between vehicles that are traveling in the opposite direction to each other. Sideswipes from the opposite direction can increase the magnitude of the impact, increasing the likelihood of more severe injuries.
The effects of no-collisions on injury outcomes were not the same in all the models. The negative impacts of no-collisions on non-severe injuries were comparatively higher in the CL2-based model. The parameters of no collisions had significant variation for serious injury function in the EDS-based model and non-severe injury function in the CL2- and CL3-based models. The results indicated that the majority of the no-collisions in interstate crashes involving large trucks are less likely to increase the severity of injury. However, 10.35% of the no-collisions are likely to increase the chance of serious injuries under conditions similar to EDS. Moreover, 9.68% and 20.7% of no-collisions are likely to experience non-severe injuries under conditions similar to CL2 and CL3, respectively. The majority of the crashes in CL2 and CL3 occurred on roadways with considerably higher speed limits; driving at a higher speed is likely to increase the severity of injury.
DUI is more likely to increase the chances of both non-severe and serious injuries in crash scenarios such as those in EDS-, CL2- and CL3-based models. In the CL1-based model, DUI was a significant factor for only serious injury function. The effects of DUI on serious injury were substantially higher in the CL1- and CL2-based models. The findings about the effects of DUI in this study are consistent with previous studies [
5,
13,
44]. Overturning and unbelted drivers were significant indicators of both non-severe and serious injuries in all the models. Overturning had similar effects on injury outcomes across the observations in all the models. Several studies have also indicated that overturning is more likely to be associated with more severe and fatal injuries [
8,
12,
36].
On the other hand, unbelted drivers had mixed effects on non-severe injuries across the observations in the EDS-, CL1- and CL2-based models. In most cases, unbelted drivers are likely to increase the chance of non-severe injuries. However, 22.54%, 10.73%, and 23.76% of interstate crashes involving large trucks and unbelted drivers are less likely to sustain non-severe injuries under conditions similar to those in EDS, CL1, and CL2, respectively. On such occasions, the unbelted driver probably belonged to the large truck, whose driving seat is well protected. A couple of previous studies have indicated that unbelted drivers are more likely to increase the severity of injuries [
2,
7].
Urban interstate crashes involving large trucks are more likely to sustain less severe injuries. Under conditions similar to those of CL3, the chances of non-severe injuries are comparatively low for urban interstate crashes involving large trucks. It is possible that the chances of severe injuries are hugely reduced for urban interstate crashes because of congestion and better traffic management.
The results have indicated that the number of vehicles in crashes is a significant indicator of both non-severe and serious injuries in the EDS-, CL2-, and CL3-based models. In the CL1-based model, the vehicle count was a significant factor for only non-severe injuries. In most cases, an increase in vehicle numbers is likely to increase the chance of both non-severe and serious injuries in interstate crashes involving large trucks. Zheng et al. [
14] also indicated that an increase in the number of vehicles increases the chance of severe injuries in truck-involved crashes. In this study, the results indicated that the vehicle count can have mixed effects on non-severe injuries in interstate crashes involving large trucks. The mean and standard deviation indicated that an increase in the number of vehicles may not increase the chance of non-severe injuries in some cases, such as in CL1 and CL2. The proportion of observations that had different parameters for serious injury function in the CL2-based model was insignificant.
In addition to the aforementioned factors, some statistically significant factors were common between EDS and two cluster-based models. In crash scenarios such as in EDS, CL1, and CL2, angle collisions are more likely to reduce the chance of non-severe injuries in most cases. However, the parameters of angle collision for non-severe injury function varied across the observations in CL2. Under conditions similar to those CL2, 35.57% of the angle collisions involving large trucks on interstate roadways are more likely to experience non-severe injuries. The majority of crashes in CL2 occurred under dark–not-lighted conditions, which can reduce a driver’s visibility and increase the severity of injury. Other types of collisions (i.e., hitting pedestrians and backing up), the involvement of commercial trucks, speed limits, and tailgating were significant predictors of injury outcomes in EDS-, CL1- and CL3-based models. The parameters of other types of collisions were stable, and other types of collisions are more likely to increase the chances of serious injuries in crash scenarios such as in EDS, CL1, and CL3. Interstate crashes involving commercial large trucks are likely to reduce the chances of serious injuries under conditions similar to EDS. On the other hand, interstate crashes involving commercial large trucks are more likely to sustain non-severe injuries under conditions similar to those in CL1 and CL3 in the majority of occasions. However, the parameters of commercial trucks were not the same across the observations in CL3. The mean and standard deviation indicated that nearly 36% of the interstate crashes involving large trucks are less likely to sustain non-severe injuries. In the EDS- and CL1-based models, the speed limit had a significant influence on serious and non-severe injuries and is likely to increase the chance of serious and non-severe injuries, respectively. In the CL3-based model, it increases the chance of both non-severe and serious injuries. The parameters of the speed limit in the EDS-based model varied in the EDS-based model but were insignificant. In both the EDS and CL3-based models, tailgating had negative impacts on both non-severe and serious injuries. In the CL1-based model, it had similar impacts on only non-severe injuries. However, the parameters of tailgating for both non-severe and serious injury functions were not the same across all the observations in CL3. In total, 67.58% and 87.55% of the interstate crashes involving large trucks and tailgating are less likely to lead to non-severe and serious injuries, respectively. The rest are more likely to experience non-severe and serious injuries.
Snowy weather was a significant predictor of injury outcomes in the EDS-, CL2- and CL3-based models. The effects of snowy weather on non-severe injuries were not the same across the observations in EDS, CL2 and CL3. Snowy weather is likely to reduce the chance of non-severe injuries in most cases since drivers are usually cautious in snowy weather. However, 32.30%, 24.96%, and 34.62% of interstate crashes involving large trucks in snowy weather have a probability of experiencing non-severe injuries in crash scenarios similar to those in EDS, CL2, and CL3. Most crashes in CL2 and CL3 occurred in high speed limit zones. It is possible that some drivers were driving at a higher speed, which leads to more severe injuries.
The contributory factors, which were statistically significant in more than one model, also included drivers aged between 50 and 64 years (CL1 and CL3), drivers aged over 65 years (EDS and CL3), speeding-related (EDS and CL1), and dark–not-lighted conditions (CL1 and CL2). In the CL3-based model, drivers aged between 50 and 64 years had negative impacts on non-severe injuries, and the parameters for non-severe injury function were fixed. In the CL1-based model, the parameters for non-severe injury function varied across the observations. The results indicated that 63.92% of interstate crashes involving large trucks and drivers aged between 50 and 64 years are less likely to sustain non-severe injuries, and the rest are more likely to sustain non-severe injuries under conditions similar to those in CL1. Drivers aged between 50 and 64 years not only have years of driving experience but also have some physical limitations. This may explain the mixed effects of drivers aged between 50 and 64 years on non-severe injuries. Drivers aged over 65 years are more likely to increase the severity of injuries under conditions similar to those in EDS and CL3. Moreover, their effects on injury outcomes were stable across the observations. Speeding-related interstate crashes involving large trucks are likely to increase the chance of non-severe injuries in crash scenarios similar to EDS. Such crashes are also more likely to sustain both non-severe and serious injuries under conditions similar to those in CL1. However, the parameters of the speeding-related factor for non-severe injury function were not the same across the observations in the CL1-based model. The mean and standard deviation indicated that 32.56% of speeding-related interstate crashes involving large trucks are less likely to sustain non-severe injuries. In CL1, the majority of the crashes occurred in urban areas, where traffic rules are more strictly maintained and congestion is frequent. These issues are likely to reduce the severity of the injury. In the CL1 and CL2-based models, dark–not-lighted conditions were a significant predictor for non-severe injuries. The parameters of dark–not-lighted conditions were fixed in the CL1-based model but were random in the CL2-based model. Under conditions similar to CL2, 86.40% of the interstate crashes involving large trucks under dark–not-lighted conditions are less likely to experience non-severe injuries, but the rest are more likely to experience non-severe injuries. Usually, drivers are likely to drive more cautiously under dark–not-lighted conditions. However, significant factors such as an absence of traffic control and adverse weather conditions could increase the severity of injury.
The cluster-based approach also revealed that some contributory factors were significant only in the cluster-based models. The evening hours were significant for both non-severe and serious injury functions only in the CL1-based model. However, the parameters of evening hours for both non-severe and serious injury functions varied across the observations in CL1. In total, 72.87% of the interstate crashes involving large trucks in the evening hours are more likely to experience non-severe injury, and 90.48% are less likely to sustain serious injuries under conditions such as those in CL1. Only in the CL2-based model, curved roads, dark–lighted conditions, were the presence of traffic control, other types of weather (i.e., windy, cloudy, fog, etc.) and rainy weather significant indicators of non-severe injury. The parameters of the presence of traffic control and other types of weather were stable and had negative impacts on non-severe injuries only in the CL2-based model. On the other hand, the parameters of curved roads, dark–lighted conditions, and rainy weather for non-severe injury function were not the same across the observations. The mean and standard deviation indicated that 57.71%, 72.56%, and 68.94% of interstate crashes involving large trucks on curved roads, under dark–lighted and rainy weather conditions are less likely to experience non-severe injuries under conditions similar to CL2, respectively. Lastly, drivers being distracted or fatigued/asleep were significant predictors of non-severe injuries only in the CL3-based model. The parameters of drivers being distracted or fatigued/asleep were stable and had positive impacts on non-severe injuries.