A Conceptual Framework for Mobility Data Science

The rapid digitalization of the mobility and transport ecosystem generates an escalating volume of data as a by-product, presenting an invaluable resource for various stakeholders. This mobility and transport data can fuel data-driven services, ushering in a new era of possibilities. To facilitate the development of these digitalized mobility services, we propose a novel conceptual framework for Mobility Data Science. Our approach seamlessly merges two distinct research domains: 1) mobility and transport science, and 2) data science. Mobility Data Science serves as a connective tissue, bridging the digital layers of physical mobility and transport artefacts such as people, goods, transport means, and infrastructure with the digital layer of data-driven services. In this paper, we introduce our conceptual framework, shaped by insights from domain experts deeply immersed in the mobility and transport ecosystem. We present a practical application of our framework in guiding the implementation of a driving style detection service, demonstrating its effectiveness in translating theoretical concepts into real-world solutions. Furthermore, we validate our framework's versatility by applying it to various real-world cases from the scientific literature. Our demonstration showcases the framework's adaptability and its potential to unlock value by harnessing mobility and transport data, enabling the creation of impactful data-driven services. We believe our framework offers valuable insights for researchers and practitioners: It provides a structured approach to comprehend and leverage the potential of mobility and transport data for developing impactful data-driven services, which we refer to as digitalized mobility services.