Examining behavioral and attitudinal differences among groups in their traffic safety culture

Abstract

The paper explores the concept that, for a given population, there is not a single “traffic safety culture,” but rather a set of alternative cultures in which the individual driver might belong. There are several different cultures of dangerous driving behavior and each might need a separate strategy for intervention or amelioration. First, the paper summarizes the over-arching theory explored in the research, which applies Multi-group Structural Equation Modeling (MSEM) in a modification of the Theory of Planned Behavior (TPB) in the explanation of Risky Driving Behavior, based on ten observed explanatory factors. Second, we apply Latent Class Cluster (LCC) segmentation to the full sample, revealing four segments: one cluster reflecting a “Low Risk Driving Safety Group” and three clusters describing three different groups of problematic drivers. We first apply MSEM to two groups; the “Low Risk Driving Safety Group,” and the “High Risk Driving Safety Group,” defined as the members of the three problematic clusters together, revealing how a “Low Risk” culture differs from the “High Risk” culture, with the relative importance of the TPB explanatory factors varying sharply between the two groups. Finally, the three problematic clusters are profiled for demographics and their mean scores for the ten observed explanatory factors. Each of the clusters is reviewed in terms of responses to selected survey questions. Three separate and distinct dangerous traffic safety cultures emerge: first, a culture of risky driving dominated by excitement seeking and optimism bias; a second dominated by denial of societal values; and a third characterized by its propensity to find rational justifications for its speeding behavior. The paper applies two research methods together: LCC segmentation divides our sample into meaningful subgroups, while MSEM reveals both within-group analysis of variance and between-group differences in safety attitudes and outcomes. The paper concludes that the combination of the segmentation powers of the LCC and the analysis powers of the MSEM provides the analyst with an improved understanding of the attitudes and behaviors of the separate groups, all tied back to the over-arching theory underlying the research.