Multi-agent transit operations and assignment model

Transit systems exercise complex dynamics and evolve through the interaction of various agents. The analysis of transit performance requires emulating the dynamic loading of travellers and their interaction with the underlying transit system. Multi-agent simulations aim to mimic the emergence of global spontaneous order from numerous inter-dependent local decisions. This paper presents a framework for a multi-agent transit operations and assignment model which captures supply uncertainties and adaptive user decisions. An iterative day-to-day learning process consisting of a within-day dynamic network loading loop simulates the interaction between transit supply and demand. The model requires the development and integration of several modules including traffic simulation, transit operations and control, dynamic path choice model and real-time information generator. BusMezzo, a transit simulation model, is used as the platform for implementation.