Modeling Social Influence on Activity-Travel Behaviors Using Artificial Transportation Systems

A deep understanding of people's activity-travel behaviors is critical and essential for effective travel demand forecasting and management. Although it is acknowledged that social interactions play an important role in people's decision-making behaviors, our understanding of how they shape and impact activity-travel behaviors of people is still limited. Therefore, for the first time, this paper introduces social learning into artificial transportation systems (ATSs) to model their influence on activity-travel behaviors. Based on a specified ATS, three types of universal social interactions (i.e., imitation, conformity, and experience sharing on social networks) are modeled and studied. The results indicate that our models can make artificial agents learn to decide the best behavior, form habitual choices, and emerge fashion gradually.