The influence of data aggregation levels on accident severity researches

Abstract

Abstract This paper aims at assessing the influence of data aggregation on the estimation of the accidents’ severity. For this purpose, 27,400 accidents are investigated on three aggregation levels: individually (idl) which is highly disaggregated hourly (hrl), and daily (dil). A Severity Score (SS) is defined based on the injuries’ severity and relative cost weights. Multiple Linear Regressions are developed by assessing the SS’s variation as a collective explanatory effect of 83 predictors related to road environment, weather and lighting conditions, adherence conditions, and number of the involved vehicles. F and t tests are employed in order to assess the developed models and to investigate which variable is of statistical significance. This research shows that the higher the level of aggregation is, the higher the coefficient of determination. SS_dil model has the higher R2 value and direct relationships between SS and the number of accidents occurring in “U” curves, on roads with no shoulders, where the roadway was covered with slime or black ice and where the roadway was wet was found. One of the interesting findings of this paper shows that in the models with low R2 values (based on individual and hourly aggregated data), snow and fog significantly explain the variation of SS. As these are seasonally and short time phenomena, a further research should be focused on how the estimated models based on individual or hourly aggregated data could be improved by adding other significant predictors related to driver’s and vehicle’s characteristics and road environment’ features.