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Modeling the Effects of Social Influence on Driver Behavior in Applying Child Vehicle Safety Restraint

In recent work we introduced an agent based model prototype to enable intervention studies by simulating the effect of the use of vehicle restraint systems on injury levels in children passengers. In a socially motivated dynamic framework, modeled drivers, or agents, are able to identify kin and neighbor relations. A knowledge structure to capture the driver's knowledge of the perceived correct child seat selection and location configuration is implemented. In this study, using a cultural algorithm, with situational knowledge dominant in the belief space, we enable both positive and negative exemplars in order to guide the belief at the population level. Based on evolving individual experiences, and corresponding changes in the belief system, the presence of both positive and negative exemplars were shown to be influential on the overall children population health and improved driver correctness in selecting the correct child restraint.