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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.

The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform.

According to the COVID-19 lockdown and successive reopening a number of facts can be analysed. The main effects have been detected on: mobility and environment, and specifically on traffic, environmental data and parking. The mobility reduction has been assessed to be quite coherent with respect to what has been described by Google Global mobility report. On the other hand, in this paper a number of additional aspects have been put in evidence providing detailed aspects on mobility and parking that allowed us to better analyse the impact of the reopening on an eventual revamping of the infection, also taking into account of the Rt index. To this end, the collected data from the field have been compared from those of Google and some considerations with respect to the Imperial college Report 20 have been derived. For the pollutant aspects, a relevant reduction on most of them has been measured and rationales are reported. The solution has exploited the Snap4City IOT smart city infrastructure and data collector and Dashboard in place in Tuscany.