  The advent of intelligent vehicles that can communicate with infrastructure
as well as automate the movement provides a range of new options to address key
urban traffic issues such as congestion and pollution, without the need for
centralized traffic control. Furthermore, the advances in the information,
communication, and sensing technologies have provided access to real-time
traffic and emission data. Leveraging these advancements, a dynamic
multi-objective eco-routing strategy for connected & automated vehicles (CAVs)
is proposed and implemented in a distributed traffic management system. It is
applied to the road network of downtown Toronto in an in-house agent-based
traffic simulation platform. The performance of the proposed system is compared
to various single-objective optimizations. Simulation results show the
significance of incorporating real-time emission and traffic state into the
dynamic routing, along with considering the expected delays at the downstream
intersections. The proposed multi-objective eco-routing has the potential of
reducing GHG and NOx emissions by 43% and 18.58%, respectively, while reducing
average travel time by 40%.
