  In this study, we propose a three-stage framework for the planning and
scheduling of high-capacity mobility-on-demand services (e.g., micro transit
and flexible transit) at urban activity hubs. The proposed framework consists
of (1) the route generation step to and from the activity hub with connectivity
to existing transit systems, and (2) the robust route scheduling step which
determines the vehicle assignment and route headway under demand uncertainty.
Efficient exact and heuristic algorithms are developed for identifying the
minimum number of routes that maximize passenger coverage, and a matching
scheme is proposed to combine routes to and from the hub into roundtrips
optimally. With the generated routes, the robust route scheduling problem is
formulated as a two-stage robust optimization problem. Model reformulations are
introduced to solve the robust optimization problem into the global optimum. In
this regard, the proposed framework presents both algorithmic and analytic
solutions for developing the hub-based transit services in response to the
varying passenger demand over a short-time period. To validate the
effectiveness of the proposed framework, comprehensive numerical experiments
are conducted for planning the HHMoD services at the JFK airport in New York
City (NYC). The results show the superior performance of the proposed route
generation algorithm to maximize the citywide coverage more efficiently. The
results also demonstrate the cost-effectiveness of the robust route schedules
under normal demand conditions and against worst-case-oriented realizations of
passenger demand.
