Analysis of large truck crash severity using heteroskedastic ordered probit models

Abstract

Long-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States' Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for two-trailer LCVs is higher than that of single-trailer nonLCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks. Submitted for publication to Accident Analysis & Prevention -March 2010 ABSTRACTLong-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States" Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for twotrailer LCVs is higher than that of single-trailer non-LCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks. Manuscript Click here to view linked ReferencesLemp, Kockelman, and Unnikrishnan 2

Injury severity models for motor vehicle accidents: a review

“…j Heteroskedastic probit model (HOP): this is used when the error terms are not homoskedastic and their variance may be parametrised as a function of covariates. HOPs offers more flexibility than OPMs, since they capture the effect of the independent variables on the variance or uncertainty in the outcome (Lemp et al, 2011). j Bayesian ordered probit model (BOP): this is an extension of the Bayesian inference into the OPM, in which the parameters to be estimated are assumed to follow certain prior distributions.…”

“…Two studies used the LL as the fit test. However, only Lemp et al (2011) used it to compare two models (OPM against HOP).…”

“…BOP and OPM produced similar results for large data size; the authors recommended using BOPs for smaller data sizes, as they can produce more reasonable parameter estimation and better prediction performance. In contrast, when comparing OPMs with HOPs, the HOP was preferred over the OPM in terms of log-likelihoods (Lemp et al, 2011). …”

Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas

“…The response variable vehicle damage level is inherently ordinal discrete, so a categorical response model is used to identify the factors affecting it. Commonly, statistical formulation to model vehicle damage level is the ordered response formulation, especially ordered logit model or ordered probit model [4,5,6,7,8,9,10,11,12,13]. The main difference between the two ordered models is the assumption of distribution of the error term.…”

From the analysis of European accident data to safety assessment for planning: the role of good vehicles in urban area

“…Trucks tend to produce serious consequences when involved in collisions with passenger cars or pedestrians. A number of studies has examined truck involvement in road accidents (Lemp et al [15]; Gitelman et al [16]), although only few of them have focussed upon accidents in urban areas. As discussed in the following, although the level of detail of the main European safety statistics (e.g.…”