Analysis of large truck crash severity using heteroskedastic ordered probit models

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• 作者：Jason D. Lemp ; Kara M. Kockelman ; Avinash Unnikrishnan
• 关键词：Long-combination vehicles ; Crash severity ; Heavy-duty truck safety ; Heteroskedasticity ; Ordered probit
• 刊名：Accident Analysis and Prevention
• 出版年：2011
• 出版时间：January 2011
• 年：2011
• 卷：43
• 期：1
• 页码：370-380
• 全文大小：539 K

文摘

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.