  On-demand ridepooling (ODRP) can become a powerful alternative to reduce
congestion and emissions, if it attracts private car users. Therefore, it is
crucial to identify the strategic phenomena that determine when ODRP systems
can run efficiently. In this paper, we analyze the performance of an ODRP
system, in which the fleet of low-capacity vehicles is endogenously adapted to
the demand, and operated in a zone covered by a single transit line. The
routing of the on-demand fleet follows some of the rules of public transport
systems; namely, it is not-for-profit, some users can be required to walk, and
all requests must be served. Considering both users' and operators' costs we
identify two sources of scale economies: when demand grows, the average cost is
reduced due to a) an equivalent of the Mohring Effect (also present in public
transport), and b) due to matching users with more similar routes when they are
assigned to the vehicles, which we call Better-matching Effect. A
counter-balance force, called Flex-route Effect, is observed when the vehicle
loads increase and users face longer detours. We find a specific demand range
in which the latter effect dominates the others, imposing diseconomies of scale
when only users' costs are considered. Such a phenomenon emerges because the
routes are not fixed; hence, it is not observed in traditional public transport
systems. However, when considering both users' and operators' costs, scale
economies prevail. Our simulations show that relaxing door-to-door vehicle
requirements to allow short walks is crucial for the performance of ODRP. In
fact, we observe that an ODRP system with human-driven vehicles and walks
allowed has a total cost at a similar level to that of a door-to-door ODRP
system with driverless vehicles.
