Optimizing mobility services is one of the greatest challenges Smart Cities face in their efforts to improve residents’ wellbeing and reduce emissions. The advent of IoT has created unparalleled opportunities to collect large amounts of data about how people use transportation. This data could be used to ascertain the quality and reach of the services offered and to inform future policy—provided cities have the capabilities to process, curate, integrate and analyse the data effectively. At the same time, to be truly ‘Smart’, cities need to ensure that the data-driven decisions they make reflect the needs of their citizens, create feedback loops, and widen participation. In this chapter, we introduce QROWD, a data integration and analytics platform that seamlessly integrates multiple data sources alongside human, social and computational intelligence to build hybrid, automated data-centric workflows. By doing so, QROWD applications can take advantage of the best of both worlds: the accuracy and scale of machine computation, and the skills, knowledge and expertise of people. We present the architecture and main components of the platform, as well as its usage to realise two mobility use cases: estimating the modal split, which refers to trips people take that involve more than one type of transport, and urban auditing.