Applying an Agent-Based Social Network in Travel Forecasting with Effects on Disease Spread

Travel demand models benefit from integrating social networks to capture socially induced travel behavior, such as destination choice. One of the obstacles to this integration is the lack of reliable and realistic social networks as input. This paper introduces an agent-based method to synthesize social networks with global characteristics and heterogeneous ego-centric homophilies (distance, age, gender) for a substantial population (approximately (Formula presented.) individuals). Drawing on data from an ego-centric snowball sample, it successfully replicates age, gender, and distance homophilies while achieving significant social network-level transitivity and clique structures. The study uses epidemic spread as a case study to explore the effects of incorporating social networks and coordinated destination choices into travel forecasting. Results show that while the total number of infections remains unchanged, the integration of social networks and coordinated destination choices affects contextual aspects of infection events. Coordinated travel through social networks accelerates the initial spread and affects the spatial distribution of epidemics. The findings underscore the importance of integrating social networks and joint travel to more accurately represent social travel behavior.