  The mobility of people is at the center of transportation planning and
decision-making of the cities of the future. In order to accelerate the
transition to zero-emissions and to maximize air quality benefits, smart cities
are prioritizing walking, cycling, shared mobility services and public
transport over the use of private cars. Extensive progress has been made in
autonomous and electric cars. Autonomous Vehicles (AV) are increasingly capable
of moving without full control of humans, automating some aspects of driving,
such as steering or braking. For these reasons, cities are investing in the
infrastructure and technology needed to support connected, multi-modal transit
networks that include shared electric Autonomous Vehicles (AV). The
relationship between traditional public transport and new mobility services is
in the spotlight and need to be rethought. This paper proposes an agent-based
simulation framework that allows for the creation and simulation of mobility
scenarios to investigate the impact of new mobility modes on a city daily life.
It lets traffic planners explore the cooperative integration of AV using a
decentralized control approach. A prototype has been implemented and validated
with data of the city of Trento.
