  Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the
urban mobility landscape, bringing disruptive changes to transportation systems
worldwide. This calls for a simulation framework where researchers from various
and across disciplines may introduce models aimed at representing the dynamics
of platform-driven urban mobility systems. In this work, we present MaaSSim, an
agent-based simulator reproducing the transport system used by two kind of
agents: (i) travellers, requesting to travel from their origin to destination
at a given time, and (ii) drivers supplying their travel needs by offering them
rides. An intermediate agent, the platform, allows demand to be matched with
supply. Agents are decision makers, specifically, travellers may decide which
mode they use or reject an incoming offer. Similarly, drivers may opt-out from
the system or reject incoming requests. All of the above behaviours are
modelled through user-defined modules, representing agents' taste variations
(heterogeneity), their previous experiences (learning) and available
information (system control). MaaSSim is an open-source library available at a
public repository github.com/RafalKucharskiPK/MaaSSim, along with a set of
tutorials and reproducible use-case scenarios.
