Analysis of Speeding Behavior

Data collected from Global Positioning System technology are used to examine the variability in speeding for 147 motorists during a 5-week period. A multilevel modeling approach is used to decompose speeding behavior into four major levels of variation: interindividual variation, temporal variation, trip-level variation, and segment-level variation. Initially, a null model (i.e., excludes the explanatory variables) is estimated to assess the variations at each level. Results suggest that the driver is more a factor in speeding as the speed limit increases but that the majority of variation in speeding goes unexplained. This finding is followed by progressively including explanatory variables (e.g., age, gender, vehicle type, trips, and purpose) at each of the four levels to assess how much more of the variation in speeding can be explained. Results suggest that the reduction in unexplained variance in speeding varies markedly by speed zone and that difference indicates the disproportionately different impacts of explanatory factors.