Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 32
Book Description
Relationship of Vehicle Weight to Fatality and Injury Risk in Model Year 1985-1993 Passenger Cars and Light Trucks
Author: Relationship of Vehicle Weight to Fatality and Injury Risk in Model Year 1985-93 Passenger Cars and Light Trucks
Author: Charles J. KahanePublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 16
Book Description
Relationships Between Vehicle Size and Fatality Risk in Model Year 1985-93 Passenger Cars and Light Trucks
Author: Charles J. KahanePublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 263
Book Description
Vehicle Weight, Fatality Risk and Crash Compatibility of Model Year 1991-99 Passenger Cars and Light Trucks
Author: Charles Jesse KahanePublisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 336
Book Description
Highway Safety
Author: United States. General Accounting OfficePublisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 44
Book Description
An Assessment of the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to 1998 Model Year Passenger Cars and 1985 to 1997 Model Year Light Trucks and Vans
Author: R. M. Van AukenHearing Under the Congressional Review Act on OSHA's Methylene Chloride Rule
Author: United States. Congress. House. Committee on Education and the Workforce. Subcommittee on Workforce ProtectionsPublisher:
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 268
Book Description
Federal Register
Author: Determinants of Motor Vehicle Fatalities and Fatality Rates in Illinois
Author: Jamshid MohammadiPublisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 148
Book Description
In this study, four groups of factors were identified as those that influence fatalities in Illinois. These were divided into driver-related, vehicle-related, highway/environment, and demographics factors. Based on a descriptive analysis of fatality data, alcohol involvement, speeding, age, rural versus urban highways, divided versus undivided highways, snowy and icy days, location of crashes in relation to the intersection, and railroad crossing were identified as factors that affect fatalities. Correlation and regression analyses were conducted at the local level to identify regional differences in fatality data. Among factors related to the driver, crashes involving alcohol was perhaps the only factor that indicated a significant regional difference and some degree of correlation with higher fatalities. The correlation and regression analyses were also conducted at the State level to investigate the dependence of fatalities on the four factor groups from year to year. The results indicated that the proportion of drivers in the 16-19 year age involved in crashes contributes significantly to the fatality rate. Also, crashes involving drivers with any blood alcohol concentration level in their systems in general contribute significantly to traffic fatalities. The significance of weekday/weekend, night/day, urban/rural driving and snowy/rainy day conditions was also investigated through the correlation and regression analyses. The analyses revealed comparable results for weekday versus weekend driving. On vehicle-related factors, crashes involving passenger cars showed a strong correlation with the fatality rate. Finally, the results of a time series analysis indicated that motor vehicle fatalities are on the decline in Illinois.
Analysis of the Relationship Between Vehicle Weight
Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
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 (R2) 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 (R2) 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.