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Over the past decade, there has been a surge of interest in the transportcommunity in the application of agent-based simulation models to evaluateflexible transit solutions characterized by different degrees of short-termflexibility in routing and scheduling. A central modeling decision in thedevelopment of an agent-based simulation model for the evaluation of flexibletransit is how one chooses to represent the mode- and route-choices oftravelers. The real-time adaptive behavior of travelers is intuitivelyimportant to model in the presence of a flexible transit service, where therouting and scheduling of vehicles is highly dependent on supply-demanddynamics at a closer to real-time temporal resolution. We propose autility-based transit route-choice model with representation of within-dayadaptive travel behavior and between-day learning where station-basedfixed-transit, flexible-transit, and active-mode alternatives may bedynamically combined in a single path. To enable experimentation, thisroute-choice model is implemented within an agent-based dynamic public transitsimulation framework. Model properties are first explored in a choice betweenfixed- and flexible-transit modes for a toy network. The framework is thenapplied to illustrate level-of-service trade-offs and analyze traveler modechoices within a mixed fixed- and flexible transit system in a case study basedon a real-life branched transit service in Stockholm, Sweden.

An adaptive route choice model for integrated fixed and flexible transit systems