Analysis of the Relationship Between Vehicle Weight/Size and Safety, and Implications for Federal Fuel Economy Regulation

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This report analyzes the relationship between vehicle weight, size (wheelbase, track width, and their product, footprint), and safety, for individual vehicle makes and models. Vehicle weight and footprint are correlated with a correlation coefficient (R{sup 2}) of about 0.62. The relationship is stronger for cars (0.69) than for light trucks (0.42); light trucks include minivans, fullsize vans, truck-based SUVs, crossover SUVs, and pickup trucks. The correlation between wheelbase and track width, the components of footprint, is about 0.61 for all light vehicles, 0.62 for cars and 0.48 for light trucks. However, the footprint data used in this analysis does not ... continued below

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Wenzel, Thomas P. March 2, 2010.

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This report analyzes the relationship between vehicle weight, size (wheelbase, track width, and their product, footprint), and safety, for individual vehicle makes and models. Vehicle weight and footprint are correlated with a correlation coefficient (R{sup 2}) of about 0.62. The relationship is stronger for cars (0.69) than for light trucks (0.42); light trucks include minivans, fullsize vans, truck-based SUVs, crossover SUVs, and pickup trucks. The correlation between wheelbase and track width, the components of footprint, is about 0.61 for all light vehicles, 0.62 for cars and 0.48 for light trucks. However, the footprint data used in this analysis does not vary for different versions of the same vehicle model, as curb weight does; the analysis could be improved with more precise data on footprint for different versions of the same vehicle model. Although US fatality risk to drivers (driver fatalities per million registered vehicles) decreases as vehicle footprint increases, there is very little correlation either for all light vehicles (0.01), or cars (0.07) or trucks (0.11). The correlation between footprint and fatality risks cars impose on drivers of other vehicles is also very low (0.01); for trucks the correlation is higher (0.30), with risk to others increasing as truck footprint increases. Fatality risks reported here do not account for differences in annual miles driven, driver age or gender, or crash location by vehicle type or model. It is difficult to account for these factors using data on national fatal crashes because the number of vehicles registered to, for instance, young males in urban areas is not readily available by vehicle type or model. State data on all police-reported crashes can be used to estimate casualty risks that account for miles driven, driver age and gender, and crash location. The number of vehicles involved in a crash can act as a proxy of the number of miles a given vehicle type, or model, is driven per year, and is a preferable unit of exposure to a serious crash than the number of registered vehicles. However, because there are relatively few fatalities in the states providing crash data, we calculate casualty risks, which are the sum of fatalities and serious or incapacitating injuries, per vehicle involved in a crash reported to the police. We can account for driver age/gender and driving location effects by excluding from analysis crashes (and casualties) involving young males and the elderly, and occurring in very rural or very urban counties. Using state data on all police-reported crashes in five states, we find that excluding crashes involving young male and elderly drivers has little effect on casualty risk; however, excluding crashes that occurred in the most rural and most urban counties (based on population density) increases casualty risk for all vehicle types except pickups. This suggests that risks for pickups are overstated unless they account for the population density of the county in which the crashes occur. After removing crashes involving young males and elderly drivers, and those occurring in the most rural and most urban counties, we find that casualty risk in all light-duty vehicles tends to increase with increasing weight or footprint; however, the correlation (R{sup 2}) between casualty risk and vehicle weight is 0.31, while the correlation with footprint is 0.23. These relationships are stronger for cars than for light trucks. The correlation between casualty risk in frontal crashes and light-duty vehicle wheelbase is 0.12, while the correlation between casualty risk in left side crashes and track width is 0.36. We calculated separately the casualty risks vehicles impose on drivers of the other vehicles with which they crash. The correlation between casualty risk imposed by light trucks on drivers of other vehicles and light truck footprint is 0.15, while the correlation with light truck footprint is 0.33; risk imposed on others increases as light truck weight or footprint increases. Our analysis indicates that, after excluding crashes involving young male and elderly drivers, and crashes in very rural and very urban counties, and accounting for vehicle weight and footprint, sports cars, pickup trucks and truck-based SUVs have higher risk to their drivers than cars, while import luxury cars and crossover SUVs have lower risk to their drivers than cars. Similarly, pickups and sports cars impose a large casualty risk on drivers of other vehicles, after accounting for vehicle weight and footprint. Our analysis suggests that excluding young male and elderly drivers, and crashes in very rural and urban counties, accounting for vehicle weight, footprint, and type explains only about half of the variability in casualty risk to drivers, and to drivers of other vehicles, by vehicle model.

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  • Report No.: LBNL-3143E
  • Grant Number: DE-AC02-05CH11231
  • DOI: 10.2172/982927 | External Link
  • Office of Scientific & Technical Information Report Number: 982927
  • Archival Resource Key: ark:/67531/metadc1013202

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  • March 2, 2010

Added to The UNT Digital Library

  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 17, 2017, 5:59 p.m.

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Wenzel, Thomas P. Analysis of the Relationship Between Vehicle Weight/Size and Safety, and Implications for Federal Fuel Economy Regulation, report, March 2, 2010; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1013202/: accessed August 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.