Considering that sustainable urban mobility is one of the main challenges facing urban centers internationally, the aim of this research is the evidence-based support of new sustainable and environmentally friendly forms of urban mobility, utilizing and analyzing big data. Emphasis will be given on combined transport services, ride-sharing and urban transport, giving priority to active mobility such as walking, cycling, micro-mobility services, etc. and in Public Transport. For this purpose, big data will be collected from a series of modern applications such as smartphone sensors, telematics applications, traffic management centers, etc. Advanced machine learning and artificial intelligence algorithms will be developed to harness the data and innovative sophisticated statistical models will be created. This approach enables real-time, precise monitoring and evaluation of both current and emerging sustainable urban mobility policies and services. It also empowers the development of innovative interventions and actions to further bolster the sustainability of urban mobility.