Article ID: | iaor2004284 |
Country: | United States |
Volume: | 34 |
Issue: | 5 |
Start Page Number: | 673 |
End Page Number: | 687 |
Publication Date: | Sep 2002 |
Journal: | Accident Analysis and Prevention |
Authors: | Scuffham P.A., Langley J.D. |
Keywords: | statistics: general, medicine |
The aim of this study was to examine the changes in the trend and seasonal patterns in fatal crashes in New Zealand in relation to changes in economic conditions between 1970 and 1994. The Harey and Durbin structural time series model (STSM), an ‘unobserved components’ class of model, was used to estimate models for quarterly fatal traffic crashes. The dependent variable was modelled as the number of crashes and three variants of the crash rate (crashes per 10,000 km travelled, crashes per 1000 vehicles, and crashes per 1000 population). Independent variables included in the models were unemployment rate (UER), real gross domestic product per capita, the proportion of motorcycles, the proportion of young males in the population, alcohol consumption per capita, the open road speed limit, and dummy variables for the 1973 and 1979 oil crises and seat belt wearing laws. UERs, real GDP per capita, and alcohol consumption were all significant and important factors in explaining the short-run dynamics of the models. In the long-run, real GDP per capita was directly related to the number of crashes but after controlling for distance travelled was not significant. This suggests increases in income are associated with a short-run reduction in risk but increases in exposure to a crash (i.e. distance travelled) in the long-run. A 1% increase in the open road speed limit was associated with a long-run 0.5% increase in fatal crashes. Substantial reductions in fatal crashes were associated with the 1979 oil crisis and seat belt wearing laws. The 1984 universal seat belt wearing law was associated with a sustained 15.6% reduction in fatal crashes. These road policy factors appeared to have a greater influence on crashes than the role of demographic and economic factors.