We present an agent-based model of a public bus where some passengers board and exit at every stop. Agents are the passengers characterized by their ethnicity, age group and cultural backgrounds and respective preferences for seats in the bus. Different scenarios are setup whereby the bus moves in various parts of the city, which maybe segregated residentially, university campuses, and areas of mixed background. Emergence of clusters maybe observed as passenger agents make decision and adapt their strategies. Measures for such emergent clusters are applied on a set of scenarios. Utilization of the bus seats are also studied from the simulated experiments. The current model is scalable to many buses running in the city. Moreover, as next step, human intervention would be possible where human agents queued along with the software agents could make their own choices for the seats in the bus.
Keywords: agent -based simulation, seating preference, average clustering size, model-to-model comparison