The movement of people from one place to another is a habitual freedom that is given by nature. Movement is therefore a crucial factor in determining the quality of life, especially in urban areas where mobility can be restricted. The ability to move is thus one of the most important fabrics of the city. It enables people to exercise freely and carry out their daily activities. Notwithstanding the important role that mobility plays in the urban fabric, mobility in many cities has long been and continues to be faced with major challenges. These challenges arise not only on an urban scale but also on a regional scale. Given the recurring mobility challenges in urban regions, the call for sustainable urban mobility has been recognised, with a focus on organising urban transport to meet the objectives of wider accessibility, reduced private car dependency, use of sustainable energy, and reduction of carbon emissions, thereby improving the quality of life in cities or urban communities.
This research project focusses on so-called new or smart mobility services including digital ride hailing systems, e-scooters, and others. The project is largely interdisciplinary in nature and aims at five different objectives. The first goal is to identify a suitable method for extracting mobility patterns of new mobility services from social media data. The second objective is to establish a link between the disclosed patterns and available sustainability indicators. The third objective is to identify factors driving the integration of new mobility services with existing mobility landscapes. The fourth objective is to derive a formal model that represents smart, conventional and holistic mobility integration. The fifth objective is to understand the past, present and future institutional integration of the new mobility services into existing mobility planning processes. In summary, the above-mentioned objectives are to be achieved by applying a battery of different methodological approaches of both qualitative and quantitative nature. These will include a systematic literature review, social media harvesting, natural language processing, spatial autocorrelation and regression analysis, modelling of system dynamics, and interviews.