  With the prevalence of MaaS systems, route choice models need to consider
characteristics unique to them. MaaS systems tend to involve service systems
with fleets of vehicles; as a result, the available service capacity depends on
the choices of other travelers in different parts of the system. We model this
with a new concept of "congestible capacity"; that is, link capacities are a
function of flow instead of link costs. This dependency is also non-separable;
the capacity in one link can depend on flows from multiple links. An
offline-online estimation method is introduced to capture the structural
effects that flows have on capacities and the resulting impacts on route choice
utilities. The method is first applied to obtain unique congestible capacity
shadow prices in a multimodal network to verify the capability to capture
congestion effects on capacities. The capacities are shown to vary and impact
the utility of a route. The method is validated using real system data from
Citi Bike in New York City. The results show that the model can fit to the data
quite well and performs better than a baseline modeling approach that ignores
congestible capacity effects. By relating the route choice to congestible
capacities using a random utility model, modelers can monitor and quantify the
impacts to traveler consumer surplus in real time. Applications of the model
and online method include monitoring capacity effects on consumer surplus,
using the model to direct incentives programs for rebalancing and other revenue
management strategies, and to guide resource allocation to mitigate consumer
surplus impacts due to disruptions from incidents.
