The current research focuses on incorporating Autonomous Buses (ABs) in the street functional classification system. Specifically, we propose the creation of a new street type that will be strictly devoted to pedestrians, cyclists, ABs and micromobility modes (e.g. e-scooters), through a data-driven approach. The proposed method consists of five steps and takes into consideration various criteria referring both to urban (e.g. population density, school facilities, public spaces, commercial sites) and transport environment (e.g. roadway width, slopes). It is carried out by using GIS tools. The study area of the research is the city of Kallithea, a densely populated suburb in the southern part of Athens. The suggested planning approach is expected to shape favourable conditions for improving public transport efficiency and visibility by gradually incorporating low-speed, electric, pod-like ABs in a car-dominated system and providing them with a test-bed that will prepare them for their expansion to highway environments, where they would really make a difference in the long term. This is something that adds to the bigger picture of sustainable and socially inclusive transport provision even if route planning implementation is a challenging issue, since ABs should be integrated in the transport network, along with pedestrians and cyclists. The development of a method which introduces ABs in the urban transport system is in line with the emerging Mobility as a Service (MaaS) concept, and it also contributes considerably to the shift from conventional to smart cities.