As ongoing urbanization increases worldwide, cities face the challenge of accommodating growing populations while maintaining efficient and sustainable transportation systems. The emergence of connected and autonomous vehicles promises transformative changes in urban mobility. This paper outlines developments and innovations that aim to seamlessly integrate autonomous vehicles into the complex ecosystem of urban mobility. This is part of the Horizon Europe project CONDUCTOR [1], which aims to design, integrate and demonstrate advanced traffic and fleet management that enables efficient and globally optimal transportation of passengers and goods, while ensuring seamless multimodality and interoperability, through dynamic balancing and priority-based management of automated and conventional vehicles. We build on the fleet and traffic management solutions in the CCAM ecosystem. We will develop the next generation of multi-level simulation models and tools enabled by AI and data fusion. These will enhance the capabilities of transportation authorities and operators to become true conductors of future mobility networks. We will apply and test a portfolio of enhanced solutions in complementary environments in different regions. These include use cases in five European countries (Greece, Italy, Slovenia, Spain and the Netherlands): Integrated Traffic Management (demonstrated in Athens, Madrid and Almelo), Demand-Responsive Transportation (between Slovenian cities and Italian airports) and Urban Logistics (in Madrid). The CONDUCTOR project final results are expected to demonstrate that delays at traffic lights are reduced, door-to-door travel times are shortened and passengers have a better experience of public transport. It is also expected to improve the quality of transport services currently offered and reduce the length of traffic queues. The project's innovations will therefore lead to smoother urban transportation, less pollution and a higher quality of life. The authors acknowledge the financial support of the Slovenian Research and Innovation Agency under research core funding No. P2-0098. This work is also part of a project that has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101077049 (CONDUCTOR).