Numerous driver, vehicle, roadway, and environmental factors contribute to crash-injury severity. In addition to main effects, interactions between factors are very likely to be significant. The large number of potentially important factors, combined with the complex nature of crash etiology and injury outcome, present significant challenges to the safety analyst, who must select from a large number of factors and specify a comprehensive but feasible set of main factors and interactions for testing in statistical models. In addition, some factors contain a relatively large number of categories (e.g., weather conditions), and the selection of cut-off points for categorization of continuous factors may not be readily obvious (e.g., driver age). It is also important that statistical tests underlying these analyses accurately address the frequent problem of data sparseness. The development and testing of a variable-selection procedure to address each of these problems is the stated objective. Bus-involved crash data for Freeway 1 in Taiwan from 1985 through 1993 were used to screen a set of 39 possible influential factors, along with interactions. The final log-linear model shows that late-night or early-morning driving increases the risk for bus drivers of being severely injured, particularly when the drivers caused the accident or when the drivers were involved in rear-end accidents. Bus accidents involving large trucks or tractor-trailers also increase the risk. An assessment of the importance of considering interactions in crash models is presented as a conclusion.
Injury severity analysis of truck-involved crashes under different weather conditions
