A Cultural Algorithm to Guide Driver Learning in Applying Child Vehicle Safety Restraint

In previous work we introduced an agent based model prototype to simulate the effect of the use of vehicle restraint systems on injury levels in children passengers. The goal is to better understand driver behavior in deciding on using, and correctly selecting, the constraint type and matching location in the vehicle for the child. In this study we enable drivers to learn from their individual driving experience with an option for immediate feedback from an expert intervention source following an accident. After implementing a social network that enables a driver to identify kin and neighbor relations in its community, the driver then may reciprocate this knowledge either by affecting related individuals or being influenced by others´ beliefs. A Cultural Algorithm is designed to enable population level learning and to capture dominant social beliefs among drivers. Situational knowledge is implemented in the belief space based on top performing exemplars; those with demonstrated skillful use of child restraints following an accident with the least injury outcome are retained. We show that the presence of a cultural belief system positively influences the system performance measured in terms of the correctness of the use of child vehicle safety restraint and the injury level in children from vehicle accidents. However, the presence of culture made the population more resilient to changes after an intervention. This suggests that culture plays an important role in carrying out a successful intervention by health care practitioners.