On-demand ridepooling (ODRP) can become a powerful alternative to reduce congestion and emissions, if it attracts private car users rather than from public transport . Therefore, it is crucial to identify the strategic phenomena that determine when ODRP systems can run efficiently , and understand when they could be integrated into a public transport network . In this paper, we analyze the performance of an ODRP system  operated in a zone covered by a single public transport line. The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups 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 this last effect dominates the others, imposing diseconomies of scale when only users' costs are considered. Such a phenomenon  is not observed in  public transport systems based on fixed routes . However, when considering both users' and operators' costs, scale economies prevail. We compare the ODRP results against public transport, for a feeder line and a circular line with homogeneous demand. We show that ODRP is more competitive when users share a common destination (the feeder line) and when the demand is low, although scale effects suggest that ODRP can also play a role when the demand is high. Relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles , if the ODRP must serve all requests .
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 .
D
rather than from public transport
D
, and understand when they could be integrated into a public transport network
A
, in which the fleet of low-capacity vehicles is endogenously adapted to the demand, and
R
public transport
transit
R
fleet of low-capacity vehicles is endogenously adapted to the demand
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
D
,
A
a)
R
that is
also
A
b)
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the users in more compatible groups
users with more similar routes
R
this last
the latter
A
emerges because the routes are not fixed; hence, it
A
traditional
D
based on fixed routes
R
We compare the ODRP results against public transport, for a feeder line and a circular line with homogeneous demand. We show that ODRP is more competitive when users share a common destination (the feeder line) and when the demand is low, although scale effects suggest that ODRP can also play a role when the demand is high. Relaxing
Our simulations show that relaxing
R
, is shown to be crucial for ODRP to become a viable alternative for both
is crucial for the performance of ODRP. In fact, we observe that an ODRP system with
R
and automated vehicles , if the ODRP must serve all requests
vehicles and walks allowed has a total cost at a similar level to that of a door-to-door ODRP system with driverless vehicles