title: 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 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.
authors.affiliation: Cambridge Systematics, 9015 Mountain Ridge Drive, Suite 210, Austin, TX 78759 USA. lemp5000@yahoo.com
authors.forename: Jason D
authors.initials: JD
authors.lastname: Lemp
authors.forename: Kara M
authors.initials: KM
authors.lastname: Kockelman
authors.forename: Avinash
authors.initials: A
authors.lastname: Unnikrishnan
mesh_terms: D000062:Accidents, Occupational / Q000191:economics / Q000401:mortality*; D000063:Accidents, Traffic / Q000191:economics / Q000706:statistics & numerical data*; D015984:Causality; D004779:Environment Design; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D018986:Motor Vehicles / Q000191:economics / Q000706:statistics & numerical data*; D012306:Risk; D014481:United States; D014947:Wounds and Injuries / Q000401:mortality*