Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies

Advances in information and communications technologies, connected vehicle technologies, and Big Data have made it viable for public agencies to offer efficient flexible transit services for travel demand that is predominantly dynamic to the system. There is a clear gap in methodologies to evaluate the user equilibrium for flexible transport services (FTS). In this study we lay the groundwork for studying the equilibrium of these systems and propose an agent-based adjustment process to evaluate the properties of a stable state as an agent-based stochastic user equilibrium. To validate the proposed process and illustrate its effectiveness in measuring the effect of changes in FTS operating parameters on ridership three sets of experiments are conducted: (1) illustration with a simple 2-link network, (2) evaluation of a dynamic dial-a-ride problem, and (3) illustration using real data from Oakville, Ontario consisting of 57 zones and 2000 commuters.