Abstract
This study extends the two-sided day-to-day learning framework to simulate the performance of a mobility service using modular autonomous vehicles (MAVs) capable of en-route passenger transfers. An insertion heuristic is used to assign trips to a fleet of vehicles and to determine whether engaging in an en-route transfer is advantageous. The operator acts as an endogenous decision maker, updating the relative weight of the operator cost and user cost within the routing algorithm after each simulation day to optimize profit. Real transit ridership data from the United Arab Emirates are used for an empirical study of three operating strategies: door-to-door service within an urban core, commuter first/last mile service and a hub-and-spoke service. Results are compared with and without en-route transfers to quantify the advantage of the en-route transfer capability for each strategy.
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