Evaluation Methods of Dynamic Flexible Transportation Systems

demonstrates that even for a simple case the process for a single population can lead to perpetual oscillations. Nonetheless, multiple populations can be sampled to construct an invariant distribution of demand and welfare effects. The dynamic DARP evaluation successfully demonstrates that an operating policy can be integrated with the day-to-day adjustment process. Sensitivity tests from the Oakville and Manhattan experiments illustrate the effectiveness of the proposed process with respect to changes in system operating parameters and designs like fleet size, dynamic routing policy or single ride vs share-ride. We will further explore flexible transport services as two-sided markets, and extend our earlier agent-based day-to-day adjustment process to include day-to-day adjustment of the service operator(s) as a two-sided market. Computational experiments are conducted with a simple network. Findings confirm the existence of locally stable states, and of thresholds from which network externalities cause two-sided and one-sided market equilibria to diverge. We use the Ramsey pricing criterion for social optimum to show that perfectly matched states from our day-to-day process are equivalent to a social optimum. A case study using real data from Oakville, Ontario, as a first/last mile problem example demonstrates the sensitivity of the day-to-day model to operating policies.