This project within the field of mobility and transport aims to support effective traffic planning with focus on public transport by using sensors, data aggregation methods and AI. By aggregating and cleverly combining data sources from on-board sensors on public transport, from vehicle positioning data and from usage of handheld devices, mobility patterns can emerge. The goal is to enable traffic planners and other stakeholders’ deeper insights on how traffic flows in and through a region, city or municipality with higher accuracy than with traditional means of estimating mobility and traffic volumes. Ultimately, developing such a tool offers vital information on decision making regarding how and where to establish a new bus route, investigate the efficiency of an existing bus fleet as well as provide knowledge on how to further improve public transport services. The project addresses specific questions related to people traveling to Kista in the Northern Stockholm region; such as how many travel using their own cars, where do the commuters start off in the morning when going to work. A long-term goal for the users of these tools could e.g. find that some of which are individuals who just as easily could hop on a nearby commute given the right circumstances, thereby contributing to increased sustainability and a smaller overall climate footprint. This project has also looked at technical aspects of how to overcome the heterogeneous nature of the data sources to be combined, and novel methods on how to harmonize various data formats along a common geography and timeline, map matching various events and in relation with routes which are both static and dynamic and introducing third party sources such as weather conditions, dynamic time tables and other datasets of related information.